Update load.py

load.py

@@ -53,28 +53,27 @@ def load_model(fl,input):
         model = torch.load("net_gb.pt")
         print(fl)
         print(input)
-        input = torch.from_numpy(input).to(torch.float32)
+        # input = torch.from_numpy(input).to(torch.float32)
         # model.eval()
         with torch.no_grad():
             # output = model(torch.tensor([0.0,0.0,87.0,5.0,36.9,81.0,33.0,138.0,62.0,0.0,0.0,2.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,2.0,1.0,0.0,0.0,1.0,2.0,2.0,2.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,2.0,2.0,1.0]))
             output = model(torch.tensor(input))
         print(output)
         # print(len([0.0,0.0,87.0,5.0,36.9,81.0,33.0,138.0,62.0,0.0,0.0,2.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,2.0,1.0,0.0,0.0,1.0,2.0,2.0,2.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,2.0,2.0,1.0]))
-        pridect_h1_y = torch.max(output,dim=1)[1]
-        
+        pridect_h1_y = torch.max(output,dim=0)[1]
         pridect_h1_label = pridect_h1_y.data.numpy()
         print(pridect_h1_y)
-        if int(pridect_h1_label[0])==1:
+        if int(pridect_h1_label)==1:
             return "FL predict: Height."
         else:
             return "FL predict: Low."
-    else:
+    else:#55 60
         model_h1 = torch.load("net_h1.pt")
         model_h2 = torch.load("net_h2.pt")
         model_h3 = torch.load("net_h3.pt")
         print(fl)
         print(input)
-        input = torch.from_numpy(input).to(torch.float32)
+        # input = torch.from_numpy(input).to(torch.float32)
         model_h1.eval()
         model_h2.eval()
         model_h3.eval()
@@ -88,11 +87,11 @@ def load_model(fl,input):
         print(output_h3)
         # print(len([0.0,0.0,87.0,5.0,36.9,81.0,33.0,138.0,62.0,0.0,0.0,2.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,2.0,1.0,0.0,0.0,1.0,2.0,2.0,2.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,2.0,2.0,1.0]))
         # print(len(output_h1))
-        pridect_h1_y = torch.max(output_h1,dim = 1)[1]
+        pridect_h1_y = torch.max(output_h1,dim = 0)[1]
         pridect_h1_label = pridect_h1_y.data.numpy()
-        pridect_h2_y = torch.max(output_h2,dim = 1)[1]
+        pridect_h2_y = torch.max(output_h2,dim = 0)[1]
         pridect_h2_label = pridect_h2_y.data.numpy()
-        pridect_h3_y = torch.max(output_h3,dim = 1)[1]
+        pridect_h3_y = torch.max(output_h3,dim = 0)[1]
         pridect_h3_label = pridect_h3_y.data.numpy()
 
         # print(pridect_h1_y)
@@ -102,20 +101,20 @@ def load_model(fl,input):
         print(pridect_h2_label)
         print(pridect_h3_label)
         output = ""
-        if int(pridect_h1_label[0]) == 1:
+        if int(pridect_h1_label) == 1:
             print("sick")
             output +="H1 predict: Height.\n"
         else:
             print("no sick")
             output += "H1 predict: Low.\n"
-        if int(pridect_h2_label[0]) == 1:
+        if int(pridect_h2_label) == 1:
             print("sick")
             output += "H2 predict: Height.\n"
 
         else:
             print("no sick")
             output += "H2 predict: Low.\n"
-        if int(pridect_h3_label[0]) == 1:
+        if int(pridect_h3_label) == 1:
             print("sick")
             output += "H3 predict: Height.\n"
 
@@ -125,6 +124,86 @@ def load_model(fl,input):
         return output
 
 
+# def load_model(fl,input):
+#     # data_test_h1 = GetDataSet()
+#     # data_test_h1.test_data[0] = input
+#     # input = data_test_h1.test_data
+#     if fl == "Yes":
+#         model = torch.load("net_gb.pt")
+#         print(fl)
+#         print(input)
+#         input = torch.from_numpy(input).to(torch.float32)
+#         # model.eval()
+#         with torch.no_grad():
+#             # output = model(torch.tensor([0.0,0.0,87.0,5.0,36.9,81.0,33.0,138.0,62.0,0.0,0.0,2.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,2.0,1.0,0.0,0.0,1.0,2.0,2.0,2.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,2.0,2.0,1.0]))
+#             output = model(torch.tensor(input))
+#         print(output)
+#         # print(len([0.0,0.0,87.0,5.0,36.9,81.0,33.0,138.0,62.0,0.0,0.0,2.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,2.0,1.0,0.0,0.0,1.0,2.0,2.0,2.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,2.0,2.0,1.0]))
+#         pridect_h1_y = torch.max(output,dim=1)[1]
+        
+#         pridect_h1_label = pridect_h1_y.data.numpy()
+#         print(pridect_h1_y)
+#         if int(pridect_h1_label[0])==1:
+#             return "FL predict: Height."
+#         else:
+#             return "FL predict: Low."
+#     else:
+#         model_h1 = torch.load("net_h1.pt")
+#         model_h2 = torch.load("net_h2.pt")
+#         model_h3 = torch.load("net_h3.pt")
+#         print(fl)
+#         print(input)
+#         input = torch.from_numpy(input).to(torch.float32)
+#         model_h1.eval()
+#         model_h2.eval()
+#         model_h3.eval()
+#         with torch.no_grad():
+#             # output = model(torch.tensor(1.0,1.0,64.0,2.0,37.1,98.0,20.0,120.0,70.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,1.0,0.0,1.0,2.0,2.0,1.0,2.0,0.0,2.0,0.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0))
+#             output_h1 = model_h1(torch.tensor(input))
+#             output_h2 = model_h2(torch.tensor(input))
+#             output_h3 = model_h3(torch.tensor(input))
+#         print(output_h1)
+#         print(output_h2)
+#         print(output_h3)
+#         # print(len([0.0,0.0,87.0,5.0,36.9,81.0,33.0,138.0,62.0,0.0,0.0,2.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,2.0,1.0,0.0,0.0,1.0,2.0,2.0,2.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,2.0,2.0,1.0]))
+#         # print(len(output_h1))
+#         pridect_h1_y = torch.max(output_h1,dim = 1)[1]
+#         pridect_h1_label = pridect_h1_y.data.numpy()
+#         pridect_h2_y = torch.max(output_h2,dim = 1)[1]
+#         pridect_h2_label = pridect_h2_y.data.numpy()
+#         pridect_h3_y = torch.max(output_h3,dim = 1)[1]
+#         pridect_h3_label = pridect_h3_y.data.numpy()
+
+#         # print(pridect_h1_y)
+#         # print(pridect_h2_y)
+#         # print(pridect_h3_y)
+#         print(pridect_h1_label)
+#         print(pridect_h2_label)
+#         print(pridect_h3_label)
+#         output = ""
+#         if int(pridect_h1_label[0]) == 1:
+#             print("sick")
+#             output +="H1 predict: Height.\n"
+#         else:
+#             print("no sick")
+#             output += "H1 predict: Low.\n"
+#         if int(pridect_h2_label[0]) == 1:
+#             print("sick")
+#             output += "H2 predict: Height.\n"
+
+#         else:
+#             print("no sick")
+#             output += "H2 predict: Low.\n"
+#         if int(pridect_h3_label[0]) == 1:
+#             print("sick")
+#             output += "H3 predict: Height.\n"
+
+#         else:
+#             print("no sick")
+#             output += "H3 predict: Low.\n"
+#         return output
+
+
 
 # print(data_test_h1.train_data[0])
 # print(len(data_test_h1.train_data))