how can i use my saving model

[junzlovegood]

I want to use my saving model , but i get error:

Traceback (most recent call last): File "ause_model.py", line 82, in <module> model = load_model('./client_1_main_global_model.pth') File "ause_model.py", line 78, in load_model model.load_state_dict(torch.load(model_path)) File "//site-packages/torch/nn/modules/module.py", line 1482, in load_state_dict raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format( RuntimeError: Error(s) in loading state_dict for MyLSTM: Missing key(s) in state_dict: "rnn.weight_ih_l0", "rnn.weight_hh_l0", "rnn.bias_ih_l0", "rnn.bias_hh_l0", "rnn.weight_ih_l0_reverse", "rnn.weight_hh_l0_reverse", "rnn.bias_ih_l0_reverse", "rnn.bias_hh_l0_reverse", "output_layer.weight", "output_layer.bias". Unexpected key(s) in state_dict: "cur_round", "model".

here is my code:
`class MyLSTM(nn.Module):
def init(self,
in_channels,
hidden,
out_channels,
n_layers=1,
embed_size=8,
dropout=.0):
super(MyLSTM, self).init()
self.in_channels = in_channels
self.hidden = hidden
self.embed_size = embed_size
self.out_channels = out_channels
self.n_layers = n_layers

    # self.encoder = nn.Embedding(in_channels, embed_size)

    self.rnn =\
        nn.LSTM(
            input_size=in_channels,
            hidden_size=hidden,
            num_layers=n_layers,
            batch_first=True,
            dropout=dropout,
            bidirectional=True
        )

    # 双向 LSTM 输出维度为 hidden_size * 2
    self.output_layer = nn.Linear(hidden * 2, out_channels)

def forward(self, input_):
    input_ = input_.unsqueeze(1)
    output, _ = self.rnn(input_)
    lstm_out_last = output[:, -1, :]

    # 通过输出层，得到最终的预测结果
    # 输出的形状: (batch_size, output_size)
    output = self.output_layer(lstm_out_last)

    return output

加载模型

model = torch.load('final_main_global_model.pth')

def load_model(model_path):
# 创建一个与预训练模型相同结构的实例
model = MyLSTM(in_channels=23, hidden=128, out_channels=1) # 以ResNet18为例
# 加载保存的模型参数
model.load_state_dict(torch.load(model_path))
return model

model = load_model('./client_1_main_global_model.pth')

i = 0
model.eval()
with torch.no_grad():
for data, target in eval_loader:
if (i == 100):
break
# data = data.unsqueeze(1)
output = model(data)
print(output)
i += 1`

[Weirui Kuang]

Duplicated #762