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use.py

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+import torch
+from torch import nn
+
+DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+list1=[]
+list_data = "2945807302157368193036426212997220033224538323640562241549909514547527720405608656907029313758584719540613589525481454212472019860395476200753292612652064279287757447621682752174888515904584744529078454748554565275582823574162998649840329792320732021527380675691933505646185089414885945266985722969732915061599825966637476"
+# print(len(list_data))
+for i in range(1,len(list_data)):
+    list1.append(int(list_data[i]))
+# print(list1)
+list1=torch.tensor(list1, dtype=torch.float32).to(DEVICE)
+# print(list1)
+# print(list1.shape)
+
+class Model(nn.Module):
+    def __init__(self):
+        super(Model, self).__init__()
+        self.net = nn.Sequential(
+            nn.Linear(321, 200),
+            nn.ReLU(),
+            nn.Linear(200,100),
+            nn.ReLU(),
+            nn.Linear(100, 3),
+        )
+
+    def forward(self, input):
+        return self.net(input)
+
+model = torch.load("Modle_0_GPU.pth").to(DEVICE)
+
+model.eval()
+
+with torch.no_grad():
+    output = model(list1)
+
+result = output.argmax().item()
+print('这是等级{}'.format(result))
+