fix 任务为空时无法执行的bug

server/app.py

@@ -49,6 +49,8 @@ def get_max_task_id():
     
     task_list = []
     task_list = os.listdir(task_output_dir)
+    if len(task_list) == 0:
+        return 0
     # 文件夹的名称是按照task_<task_id>_...来的，取出最大的task_id
     task_id_to_task_dir = {}
     task_id_list = []
@@ -95,7 +97,7 @@ def get_result(task_id:str):
     with open(tensorflow_json, "r") as file2:
         data2 = json.load(file2)
     
-    img_root_relative_path = task_id + '/' + result_model_name + '/'
+    img_root_relative_path = "/model_accuracy_api/image/" + task_id + '/' + result_model_name + '/'
     
     
     combined_data = {

server/output/task_1_fashion2_1/fashion2/metrics_result/fashion2_D_MAD_result.csv

@@ -9,13 +9,13 @@ fashion2_origin0.h_tensorflow_mxnet_input6,1.8905183196693542e-06
 fashion2_origin0.h_tensorflow_mxnet_input7,8.76466219779104e-06
 fashion2_origin0.h_tensorflow_mxnet_input8,3.5046812172367936e-06
 fashion2_origin0.h_tensorflow_mxnet_input9,6.812222181906691e-07
-fashion2_origin0-WS1.h_tensorflow_mxnet_input0,1.1224511808904936e-06
-fashion2_origin0-WS1.h_tensorflow_mxnet_input1,3.5494170447236684e-07
-fashion2_origin0-WS1.h_tensorflow_mxnet_input2,1.7220250470018073e-07
-fashion2_origin0-WS1.h_tensorflow_mxnet_input3,3.008639396284707e-05
-fashion2_origin0-WS1.h_tensorflow_mxnet_input4,1.443948463020206e-06
-fashion2_origin0-WS1.h_tensorflow_mxnet_input5,1.452377631494528e-07
-fashion2_origin0-WS1.h_tensorflow_mxnet_input6,3.9023408504590407e-08
-fashion2_origin0-WS1.h_tensorflow_mxnet_input7,0.0
-fashion2_origin0-WS1.h_tensorflow_mxnet_input8,2.476682539054309e-06
-fashion2_origin0-WS1.h_tensorflow_mxnet_input9,7.502628704969538e-07
+fashion2_origin0-LR1.h_tensorflow_mxnet_input0,2.1494726354376326e-07
+fashion2_origin0-LR1.h_tensorflow_mxnet_input1,1.0246135389024857e-06
+fashion2_origin0-LR1.h_tensorflow_mxnet_input2,5.563522790907882e-06
+fashion2_origin0-LR1.h_tensorflow_mxnet_input3,2.2931722298835666e-07
+fashion2_origin0-LR1.h_tensorflow_mxnet_input4,6.707047646159481e-07
+fashion2_origin0-LR1.h_tensorflow_mxnet_input5,0.0020074336789548397
+fashion2_origin0-LR1.h_tensorflow_mxnet_input6,2.3664529180678073e-06
+fashion2_origin0-LR1.h_tensorflow_mxnet_input7,2.4270579501717293e-07
+fashion2_origin0-LR1.h_tensorflow_mxnet_input8,6.093952470109798e-05
+fashion2_origin0-LR1.h_tensorflow_mxnet_input9,1.2965877260739944e-07

server/output/task_1_fashion2_1/fashion2/mutant_history.txt

@@ -1 +1 @@
-fashion2_origin0-WS1.hdf5
+fashion2_origin0-LR1.hdf5

server/output/task_1_fashion2_1/fashion2/mutator_history.csv

@@ -1,5 +1,5 @@
 Name,Success,Invalid,Total
-WS,0,0,1
+WS,0,0,0
 GF,0,0,0
 NEB,0,0,0
 NAI,0,0,0
@@ -8,6 +8,6 @@ ARem,0,0,0
 ARep,0,0,0
 LA,0,0,0
 LC,0,0,0
-LR,0,0,0
+LR,1,0,1
 LS,0,0,0
 MLA,0,0,0

server/output/task_1_fashion2_1/fashion2/mxnet.json

@@ -3,18 +3,18 @@
         "model": "fashion2",
         "method": "origin0",
         "result": {
-            "Losses": 0.2744102403521538,
-            "Accuracy": 0.9029728807508945,
-            "MemoryInfoList": 0.8383903503417969
+            "Losses": 0.2770436638966203,
+            "Accuracy": 0.903104342520237,
+            "MemoryInfoList": 0.7886598110198975
         }
     },
     {
         "model": "fashion2",
-        "method": "origin0-WS1",
+        "method": "origin0-LR1",
         "result": {
-            "Losses": 0.34761693328619003,
-            "Accuracy": 0.877291165292263,
-            "MemoryInfoList": 0.8219783306121826
+            "Losses": 0.38350426964461803,
+            "Accuracy": 0.8806340135633945,
+            "MemoryInfoList": 0.7703914642333984
         }
     }
 ]

server/output/task_1_fashion2_1/fashion2/mxnet_train.json

@@ -2,129 +2,129 @@
     {
         "Iterations": 1,
         "result": {
-            "Losses": 0.4872690439224243,
-            "Accuracy": 0.8408203125,
-            "MemoryInfoList": 0.4745445251464844
+            "Losses": 0.6776523590087891,
+            "Accuracy": 0.84375,
+            "MemoryInfoList": 0.4724922180175781
         }
     },
     {
         "Iterations": 2,
         "result": {
-            "Losses": 0.3547583818435669,
-            "Accuracy": 0.875,
-            "MemoryInfoList": 0.5845527648925781
+            "Losses": 0.5669926404953003,
+            "Accuracy": 0.86328125,
+            "MemoryInfoList": 0.5633506774902344
         }
     },
     {
         "Iterations": 3,
         "result": {
-            "Losses": 0.3730500340461731,
-            "Accuracy": 0.869140625,
-            "MemoryInfoList": 0.6955375671386719
+            "Losses": 0.43404725193977356,
+            "Accuracy": 0.8623046875,
+            "MemoryInfoList": 0.6146812438964844
         }
     },
     {
         "Iterations": 4,
         "result": {
-            "Losses": 0.38311153650283813,
-            "Accuracy": 0.8623046875,
-            "MemoryInfoList": 0.7729949951171875
+            "Losses": 0.34735798835754395,
+            "Accuracy": 0.892578125,
+            "MemoryInfoList": 0.6810455322265625
         }
     },
     {
         "Iterations": 5,
         "result": {
-            "Losses": 0.38041412830352783,
-            "Accuracy": 0.87109375,
-            "MemoryInfoList": 0.7646446228027344
+            "Losses": 0.3884972035884857,
+            "Accuracy": 0.87890625,
+            "MemoryInfoList": 0.66571044921875
         }
     },
     {
         "Iterations": 6,
         "result": {
-            "Losses": 0.356524258852005,
-            "Accuracy": 0.8662109375,
-            "MemoryInfoList": 0.8172721862792969
+            "Losses": 0.408181369304657,
+            "Accuracy": 0.8828125,
+            "MemoryInfoList": 0.7527542114257812
         }
     },
     {
         "Iterations": 7,
         "result": {
-            "Losses": 0.3416406512260437,
-            "Accuracy": 0.876953125,
-            "MemoryInfoList": 0.8653450012207031
+            "Losses": 0.34533339738845825,
+            "Accuracy": 0.880859375,
+            "MemoryInfoList": 0.7785911560058594
         }
     },
     {
         "Iterations": 8,
         "result": {
-            "Losses": 0.3707755208015442,
-            "Accuracy": 0.8725961446762085,
-            "MemoryInfoList": 0.8653450012207031
+            "Losses": 0.41461169719696045,
+            "Accuracy": 0.8581730723381042,
+            "MemoryInfoList": 0.7789268493652344
         }
     },
     {
         "Iterations": 9,
         "result": {
-            "Losses": 0.3097502589225769,
-            "Accuracy": 0.892578125,
-            "MemoryInfoList": 0.8866806030273438
+            "Losses": 0.34184765815734863,
+            "Accuracy": 0.8994140625,
+            "MemoryInfoList": 0.8022880554199219
         }
     },
     {
         "Iterations": 10,
         "result": {
-            "Losses": 0.2972621023654938,
-            "Accuracy": 0.89453125,
-            "MemoryInfoList": 0.9267616271972656
+            "Losses": 0.3411049246788025,
+            "Accuracy": 0.8818359375,
+            "MemoryInfoList": 0.8212471008300781
         }
     },
     {
         "Iterations": 11,
         "result": {
-            "Losses": 0.31856751441955566,
-            "Accuracy": 0.8896484375,
-            "MemoryInfoList": 0.8817214965820312
+            "Losses": 0.3437384366989136,
+            "Accuracy": 0.876953125,
+            "MemoryInfoList": 0.8802108764648438
         }
     },
     {
         "Iterations": 12,
         "result": {
-            "Losses": 0.32103055715560913,
-            "Accuracy": 0.884765625,
-            "MemoryInfoList": 0.9151649475097656
+            "Losses": 0.28955674171447754,
+            "Accuracy": 0.8935546875,
+            "MemoryInfoList": 0.8567848205566406
         }
     },
     {
         "Iterations": 13,
         "result": {
-            "Losses": 0.3285468816757202,
-            "Accuracy": 0.8818359375,
-            "MemoryInfoList": 0.942626953125
+            "Losses": 0.3101401627063751,
+            "Accuracy": 0.8984375,
+            "MemoryInfoList": 0.884033203125
         }
     },
     {
         "Iterations": 14,
         "result": {
-            "Losses": 0.3207525610923767,
-            "Accuracy": 0.8818359375,
-            "MemoryInfoList": 0.9426307678222656
+            "Losses": 0.31656700372695923,
+            "Accuracy": 0.8935546875,
+            "MemoryInfoList": 0.9075889587402344
         }
     },
     {
         "Iterations": 15,
         "result": {
-            "Losses": 0.3216632008552551,
-            "Accuracy": 0.88671875,
-            "MemoryInfoList": 0.8962364196777344
+            "Losses": 0.27497005462646484,
+            "Accuracy": 0.8955078125,
+            "MemoryInfoList": 0.9449958801269531
         }
     },
     {
         "Iterations": 16,
         "result": {
-            "Losses": 0.29675430059432983,
-            "Accuracy": 0.890625,
-            "MemoryInfoList": 0.9195938110351562
+            "Losses": 0.33546942472457886,
+            "Accuracy": 0.8882211446762085,
+            "MemoryInfoList": 0.9215621948242188
         }
     }
 ]

server/output/task_1_fashion2_1/fashion2/tensorflow.json

@@ -3,18 +3,18 @@
         "model": "fashion2",
         "method": "origin0",
         "result": {
-            "Losses": 0.2782079726457596,
-            "Accuracy": 0.90216064453125,
-            "MemoryInfoList": 0.796128511428833
+            "Losses": 0.2766292616724968,
+            "Accuracy": 0.902432955801487,
+            "MemoryInfoList": 0.7765240669250488
         }
     },
     {
         "model": "fashion2",
-        "method": "origin0-WS1",
+        "method": "origin0-LR1",
         "result": {
-            "Losses": 0.35277533531188965,
-            "Accuracy": 0.874417819082737,
-            "MemoryInfoList": 0.7188582420349121
+            "Losses": 0.39026222564280033,
+            "Accuracy": 0.87689208984375,
+            "MemoryInfoList": 0.7507669925689697
         }
     }
 ]

server/output/task_1_fashion2_1/fashion2/tensorflow_train.json

@@ -2,129 +2,129 @@
     {
         "Iterations": 1,
         "result": {
-            "Losses": 0.46252918243408203,
-            "Accuracy": 0.849609375,
-            "MemoryInfoList": 0.39239501953125
+            "Losses": 0.583454966545105,
+            "Accuracy": 0.8408203125,
+            "MemoryInfoList": 0.406585693359375
         }
     },
     {
         "Iterations": 2,
         "result": {
-            "Losses": 0.4296347498893738,
-            "Accuracy": 0.8447265625,
-            "MemoryInfoList": 0.499359130859375
+            "Losses": 0.5539017915725708,
+            "Accuracy": 0.87109375,
+            "MemoryInfoList": 0.5216140747070312
         }
     },
     {
         "Iterations": 3,
         "result": {
-            "Losses": 0.420149028301239,
-            "Accuracy": 0.865234375,
-            "MemoryInfoList": 0.5355758666992188
+            "Losses": 0.42033958435058594,
+            "Accuracy": 0.873046875,
+            "MemoryInfoList": 0.5802230834960938
         }
     },
     {
         "Iterations": 4,
         "result": {
-            "Losses": 0.3606413006782532,
-            "Accuracy": 0.8671875,
-            "MemoryInfoList": 0.6154556274414062
+            "Losses": 0.4039859175682068,
+            "Accuracy": 0.8818359375,
+            "MemoryInfoList": 0.6402511596679688
         }
     },
     {
         "Iterations": 5,
         "result": {
-            "Losses": 0.3635426163673401,
-            "Accuracy": 0.8603515625,
-            "MemoryInfoList": 0.6601371765136719
+            "Losses": 0.3823730945587158,
+            "Accuracy": 0.8916015625,
+            "MemoryInfoList": 0.6637535095214844
         }
     },
     {
         "Iterations": 6,
         "result": {
-            "Losses": 0.3755783438682556,
-            "Accuracy": 0.865234375,
-            "MemoryInfoList": 0.6851425170898438
+            "Losses": 0.39930737018585205,
+            "Accuracy": 0.8740234375,
+            "MemoryInfoList": 0.686187744140625
         }
     },
     {
         "Iterations": 7,
         "result": {
-            "Losses": 0.3496626019477844,
-            "Accuracy": 0.8759765625,
-            "MemoryInfoList": 0.7299652099609375
+            "Losses": 0.3784710168838501,
+            "Accuracy": 0.8828125,
+            "MemoryInfoList": 0.7126426696777344
         }
     },
     {
         "Iterations": 8,
         "result": {
-            "Losses": 0.3218604624271393,
-            "Accuracy": 0.8786057829856873,
-            "MemoryInfoList": 0.7072334289550781
+            "Losses": 0.4810777008533478,
+            "Accuracy": 0.8509615659713745,
+            "MemoryInfoList": 0.7615165710449219
         }
     },
     {
         "Iterations": 9,
         "result": {
-            "Losses": 0.33442234992980957,
-            "Accuracy": 0.8818359375,
-            "MemoryInfoList": 0.7475395202636719
+            "Losses": 0.3651778995990753,
+            "Accuracy": 0.876953125,
+            "MemoryInfoList": 0.8073310852050781
         }
     },
     {
         "Iterations": 10,
         "result": {
-            "Losses": 0.33098429441452026,
-            "Accuracy": 0.8837890625,
-            "MemoryInfoList": 0.7941474914550781
+            "Losses": 0.3714173138141632,
+            "Accuracy": 0.8740234375,
+            "MemoryInfoList": 0.8268585205078125
         }
     },
     {
         "Iterations": 11,
         "result": {
-            "Losses": 0.34452107548713684,
-            "Accuracy": 0.875,
-            "MemoryInfoList": 0.8432769775390625
+            "Losses": 0.33397021889686584,
+            "Accuracy": 0.87890625,
+            "MemoryInfoList": 0.8545455932617188
         }
     },
     {
         "Iterations": 12,
         "result": {
-            "Losses": 0.3103902041912079,
-            "Accuracy": 0.888671875,
-            "MemoryInfoList": 0.8433609008789062
+            "Losses": 0.3359469473361969,
+            "Accuracy": 0.8798828125,
+            "MemoryInfoList": 0.85455322265625
         }
     },
     {
         "Iterations": 13,
         "result": {
-            "Losses": 0.30054712295532227,
-            "Accuracy": 0.89453125,
-            "MemoryInfoList": 0.8606986999511719
+            "Losses": 0.26440030336380005,
+            "Accuracy": 0.8994140625,
+            "MemoryInfoList": 0.8780136108398438
         }
     },
     {
         "Iterations": 14,
         "result": {
-            "Losses": 0.2906036078929901,
-            "Accuracy": 0.892578125,
-            "MemoryInfoList": 0.8672676086425781
+            "Losses": 0.31551477313041687,
+            "Accuracy": 0.8896484375,
+            "MemoryInfoList": 0.9164390563964844
         }
     },
     {
         "Iterations": 15,
         "result": {
-            "Losses": 0.32827141880989075,
-            "Accuracy": 0.8935546875,
-            "MemoryInfoList": 0.8688278198242188
+            "Losses": 0.3137638568878174,
+            "Accuracy": 0.8818359375,
+            "MemoryInfoList": 0.9557762145996094
         }
     },
     {
         "Iterations": 16,
         "result": {
-            "Losses": 0.3210670053958893,
-            "Accuracy": 0.8737980723381042,
-            "MemoryInfoList": 0.851348876953125
+            "Losses": 0.3410928547382355,
+            "Accuracy": 0.8834134340286255,
+            "MemoryInfoList": 0.9459800720214844
         }
     }
 ]

server/output/task_2_fashion2_1/fashion2/metrics_result/fashion2_D_MAD_result.csv

@@ -1,21 +0,0 @@
-Mutation-Backend-Pair,Inconsistency Score
-fashion2_origin0.h_tensorflow_mxnet_input0,2.789349491649773e-05
-fashion2_origin0.h_tensorflow_mxnet_input1,2.0714646780106705e-06
-fashion2_origin0.h_tensorflow_mxnet_input2,1.3693011169380043e-06
-fashion2_origin0.h_tensorflow_mxnet_input3,3.507439600980433e-07
-fashion2_origin0.h_tensorflow_mxnet_input4,7.013139224909537e-07
-fashion2_origin0.h_tensorflow_mxnet_input5,0.0011009954614564776
-fashion2_origin0.h_tensorflow_mxnet_input6,1.8905183196693542e-06
-fashion2_origin0.h_tensorflow_mxnet_input7,8.76466219779104e-06
-fashion2_origin0.h_tensorflow_mxnet_input8,3.5046812172367936e-06
-fashion2_origin0.h_tensorflow_mxnet_input9,6.812222181906691e-07
-fashion2_origin0-LR1.h_tensorflow_mxnet_input0,3.4806015491994913e-07
-fashion2_origin0-LR1.h_tensorflow_mxnet_input1,2.343457936149207e-06
-fashion2_origin0-LR1.h_tensorflow_mxnet_input2,7.252479576891346e-07
-fashion2_origin0-LR1.h_tensorflow_mxnet_input3,0.09363756328821182
-fashion2_origin0-LR1.h_tensorflow_mxnet_input4,1.1790518783527659e-07
-fashion2_origin0-LR1.h_tensorflow_mxnet_input5,0.017461484298110008
-fashion2_origin0-LR1.h_tensorflow_mxnet_input6,6.805962129874388e-06
-fashion2_origin0-LR1.h_tensorflow_mxnet_input7,5.853631137142656e-06
-fashion2_origin0-LR1.h_tensorflow_mxnet_input8,8.788790000835434e-05
-fashion2_origin0-LR1.h_tensorflow_mxnet_input9,1.294190496992087e-07

server/output/task_2_fashion2_1/fashion2/mutant_history.txt

@@ -1 +0,0 @@
-fashion2_origin0-LR1.hdf5

server/output/task_2_fashion2_1/fashion2/mutator_history.csv

@@ -1,13 +0,0 @@
-Name,Success,Invalid,Total
-WS,0,0,0
-GF,0,0,0
-NEB,0,0,0
-NAI,0,0,0
-NS,0,0,0
-ARem,0,0,0
-ARep,0,0,0
-LA,0,0,0
-LC,0,0,0
-LR,1,0,1
-LS,0,0,0
-MLA,0,0,0

server/output/task_2_fashion2_1/fashion2/mxnet.json

@@ -1,20 +0,0 @@
-[
-    {
-        "model": "fashion2",
-        "method": "origin0",
-        "result": {
-            "Losses": 0.27659945096820593,
-            "Accuracy": 0.9029071517288685,
-            "MemoryInfoList": 0.835566520690918
-        }
-    },
-    {
-        "model": "fashion2",
-        "method": "origin0-LR1",
-        "result": {
-            "Losses": 0.3440984021872282,
-            "Accuracy": 0.8804274350404739,
-            "MemoryInfoList": 0.7985403537750244
-        }
-    }
-]

server/output/task_2_fashion2_1/fashion2/mxnet_train.json

@@ -1,130 +0,0 @@
-[
-    {
-        "Iterations": 1,
-        "result": {
-            "Losses": 0.4482508897781372,
-            "Accuracy": 0.837890625,
-            "MemoryInfoList": 0.4694023132324219
-        }
-    },
-    {
-        "Iterations": 2,
-        "result": {
-            "Losses": 0.38157153129577637,
-            "Accuracy": 0.8740234375,
-            "MemoryInfoList": 0.5575714111328125
-        }
-    },
-    {
-        "Iterations": 3,
-        "result": {
-            "Losses": 0.3769081234931946,
-            "Accuracy": 0.8662109375,
-            "MemoryInfoList": 0.6399078369140625
-        }
-    },
-    {
-        "Iterations": 4,
-        "result": {
-            "Losses": 0.4163675010204315,
-            "Accuracy": 0.8671875,
-            "MemoryInfoList": 0.689849853515625
-        }
-    },
-    {
-        "Iterations": 5,
-        "result": {
-            "Losses": 0.39336487650871277,
-            "Accuracy": 0.8779296875,
-            "MemoryInfoList": 0.71978759765625
-        }
-    },
-    {
-        "Iterations": 6,
-        "result": {
-            "Losses": 0.3816114068031311,
-            "Accuracy": 0.8681640625,
-            "MemoryInfoList": 0.7420616149902344
-        }
-    },
-    {
-        "Iterations": 7,
-        "result": {
-            "Losses": 0.31748074293136597,
-            "Accuracy": 0.8857421875,
-            "MemoryInfoList": 0.7750282287597656
-        }
-    },
-    {
-        "Iterations": 8,
-        "result": {
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