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))