Hi, I have parameters param.pth and I want to load them into model like:
model = Net()
model.load_state_dict(torch.load("param.pth"))

but it cause
KeyError: 'unexpected key "0.weight" in state_dict'

How can I load params in state_dict?
Following are details of Net and param.pth:
Network
class Net(nn.Module):
    def __init__(self):
        super(StyleNet, self).__init__()
        self.conv1 = nn.Conv2d(3,64,(3, 3),(1, 1),(1, 1))
        self.conv2 = nn.Conv2d(64,64,(3, 3),(1, 1),(1, 1))
        self.conv2_drop = nn.Dropout(0.25)
        self.pool1 = nn.MaxPool2d((4, 4),(4, 4))
        self.bn1 = nn.BatchNorm2d(64,0.001,0.9,True)
        self.conv3 = nn.Conv2d(64,128,(3, 3),(1, 1),(1, 1))
        self.conv4 = nn.Conv2d(128,128,(3, 3),(1, 1),(1, 1))
        self.conv4_drop = nn.Dropout(0.25),
        self.pool2 = nn.MaxPool2d((4, 4),(4, 4))
        self.bn2 = nn.BatchNorm2d(128,0.001,0.9,True)
        self.conv5 = nn.Conv2d(128,256,(3, 3),(1, 1),(1, 1))
        self.conv6 = nn.Conv2d(256,256,(3, 3),(1, 1),(1, 1))
        self.conv6_drop = nn.Dropout(0.25)
        self.pool3 =nn.MaxPool2d((4, 4),(4, 4))
        self.bn3 = nn.BatchNorm2d(256,0.001,0.9,True)
        self.conv7 = nn.Conv2d(256,128,(3, 3),(1, 1),(1, 1))
        self.linear1 = nn.Linear(3072,128)

    def forward(self, input):
        x = nn.ReLU(self.conv1(input))
        x = nn.ReLU(self.conv2(input))
        x = self.conv2_drop(x)
        x = self.bn1(self.pool1(x))
        x = nn.ReLU(self.conv3(x))
        x = self.conv4_drop(nn.ReLU(self.conv4(x)))
        x = self.bn2(self.pool2(x))
        x = nn.ReLU(self.conv5(x))
        x = nn.ReLU(self.conv6(x))
        x = self.conv6_drop(x)
        x = self.bn3(self.pool3(x))
        x = nn.ReLU(self.conv7(x))

        return self.linear1(x)

Keys of params.pth

in
torch.load("params.pth").keys()

out
odict_keys(['0.weight', '0.bias', '2.weight', '2.bias', '6.weight', '6.bias', '6.running_mean', '6.running_var', '7.weight', '7.bias', '9.weight', '9.bias', '13.weight', '13.bias', '13.running_mean', '13.running_var', '14.weight', '14.bias', '16.weight', '16.bias', '20.weight', '20.bias', '20.running_mean', '20.running_var', '21.weight', '21.bias', '24.1.weight', '24.1.bias'])

Thank you for dealing with it.