前端不再使用镜像仓库，直接build&添加模型精度工具

docker-compose.yml

@@ -20,6 +20,7 @@ services:
     environment:
       - SERVER_URL=http://backend:8090
       - MINIO_URL=http://minio:9000
+      - MODEL_ACCURACY_URL=http://model_accuracy_backend:5000
       - CLIENT_PORT=8000
     networks:
       - my_network

manage_platform

@@ -1 +1 @@
-Subproject commit d90931448a0109fd71ff93eb1164d6b0b4217dbf
+Subproject commit cbe2c12acfe6db4baa63dafdb483d6f6fd463ea8

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

@@ -0,0 +1,21 @@
+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-LC1.h_tensorflow_mxnet_input0,2.789349491649773e-05
+fashion2_origin0-LC1.h_tensorflow_mxnet_input1,2.0714646780106705e-06
+fashion2_origin0-LC1.h_tensorflow_mxnet_input2,1.3693011169380043e-06
+fashion2_origin0-LC1.h_tensorflow_mxnet_input3,3.507439600980433e-07
+fashion2_origin0-LC1.h_tensorflow_mxnet_input4,7.013139224909537e-07
+fashion2_origin0-LC1.h_tensorflow_mxnet_input5,0.0011009954614564776
+fashion2_origin0-LC1.h_tensorflow_mxnet_input6,1.8905183196693542e-06
+fashion2_origin0-LC1.h_tensorflow_mxnet_input7,8.76466219779104e-06
+fashion2_origin0-LC1.h_tensorflow_mxnet_input8,3.5046812172367936e-06
+fashion2_origin0-LC1.h_tensorflow_mxnet_input9,6.812222181906691e-07

model_accuracy/output/task_1_fashion2_1/fashion2/mutant_history.txt

@@ -0,0 +1 @@
+fashion2_origin0-LC1.hdf5

model_accuracy/output/task_1_fashion2_1/fashion2/mutator_history.csv

@@ -0,0 +1,13 @@
+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,0,0,1
+LR,0,0,0
+LS,0,0,0
+MLA,0,0,0

model_accuracy/output/task_1_fashion2_1/fashion2/mxnet.json

@@ -0,0 +1,20 @@
+[
+    {
+        "model": "fashion2",
+        "method": "origin0",
+        "result": {
+            "Losses": 0.27812961861491203,
+            "Accuracy": 0.9008319564163685,
+            "MemoryInfoList": 0.8638243675231934
+        }
+    },
+    {
+        "model": "fashion2",
+        "method": "origin0-LC1",
+        "result": {
+            "Losses": 0.2791674640029669,
+            "Accuracy": 0.9012216404080391,
+            "MemoryInfoList": 0.894127368927002
+        }
+    }
+]

model_accuracy/output/task_1_fashion2_1/fashion2/mxnet_train.json

@@ -0,0 +1,130 @@
+[
+    {
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+        "result": {
+            "Losses": 0.3367013931274414,
+            "Accuracy": 0.8994140625,
+            "MemoryInfoList": 0.4956092834472656
+        }
+    },
+    {
+        "Iterations": 2,
+        "result": {
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+            "Accuracy": 0.9111328125,
+            "MemoryInfoList": 0.6014060974121094
+        }
+    },
+    {
+        "Iterations": 3,
+        "result": {
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+            "Accuracy": 0.8798828125,
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+    },
+    {
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+            "Accuracy": 0.8994140625,
+            "MemoryInfoList": 0.75433349609375
+        }
+    },
+    {
+        "Iterations": 5,
+        "result": {
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+            "Accuracy": 0.908203125,
+            "MemoryInfoList": 0.8308677673339844
+        }
+    },
+    {
+        "Iterations": 6,
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+            "Accuracy": 0.900390625,
+            "MemoryInfoList": 0.8530426025390625
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+    },
+    {
+        "Iterations": 7,
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+            "Accuracy": 0.8916015625,
+            "MemoryInfoList": 0.8750267028808594
+        }
+    },
+    {
+        "Iterations": 8,
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+            "Accuracy": 0.8882211446762085,
+            "MemoryInfoList": 0.8966865539550781
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+    },
+    {
+        "Iterations": 9,
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+            "Accuracy": 0.9072265625,
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+    },
+    {
+        "Iterations": 10,
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+            "Accuracy": 0.9130859375,
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+    {
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+            "Accuracy": 0.9091796875,
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+            "Accuracy": 0.921875,
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+    {
+        "Iterations": 14,
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+            "Accuracy": 0.9013671875,
+            "MemoryInfoList": 1.0805702209472656
+        }
+    },
+    {
+        "Iterations": 15,
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+            "Accuracy": 0.892578125,
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+    {
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+            "MemoryInfoList": 1.0853652954101562
+        }
+    }
+]

model_accuracy/output/task_1_fashion2_1/fashion2/tensorflow.json

@@ -0,0 +1,20 @@
+[
+    {
+        "model": "fashion2",
+        "method": "origin0",
+        "result": {
+            "Losses": 0.2736966973170638,
+            "Accuracy": 0.9031747654080391,
+            "MemoryInfoList": 0.7473962306976318
+        }
+    },
+    {
+        "model": "fashion2",
+        "method": "origin0-LC1",
+        "result": {
+            "Losses": 0.2760988213121891,
+            "Accuracy": 0.90240478515625,
+            "MemoryInfoList": 0.7836141586303711
+        }
+    }
+]

model_accuracy/output/task_1_fashion2_1/fashion2/tensorflow_train.json

@@ -0,0 +1,130 @@
+[
+    {
+        "Iterations": 1,
+        "result": {
+            "Losses": 0.26122549176216125,
+            "Accuracy": 0.9033203125,
+            "MemoryInfoList": 0.39675140380859375
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+    {
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+    {
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+            "Accuracy": 0.88671875,
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+    {
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+    {
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+    {
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+    {
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+    }
+]