In your code example you are using a pretrained VGG model and change the classifier.
After the training you are saving the state_dict and try to load it afterwards.
While loading the state_dict you are experiencing the error. Is this correct?
Could you explain which model you’ve saved and how you’ve saved it?
I don’t get any errors using your code and the error message points to a mismatch between the keys in the state_dict. It seems your model uses keys from nn.Sequential (e.g. classifier.0.weight) while the stored state_dict has named keys e.g. classifier.fc1.weight.
TheModelClass is just a dummy model to show, how to store and load the state_dict.
You need an instance of your model to load the checkpoint.
This code works fine:
# Store model
model = models.vgg19(pretrained=True)
for param in model.parameters():
    param.requires_grad = False

classifier = nn.Sequential(OrderedDict([
    ('fc1', nn.Linear(25088, 4096)),
    ('relu1', nn.ReLU()),
    ('dropout1', nn.Dropout()),
    ('fc2', nn.Linear(4096, 102)),
    ('output', nn.LogSoftmax(dim=1))
]))

model.classifier = classifier
torch.save(model.state_dict(), 'vgg_test.pth')

# Load model
model = models.vgg19(pretrained=False)
for param in model.parameters():
    param.requires_grad = False

classifier = nn.Sequential(OrderedDict([
    ('fc1', nn.Linear(25088, 4096)),
    ('relu1', nn.ReLU()),
    ('dropout1', nn.Dropout()),
    ('fc2', nn.Linear(4096, 102)),
    ('output', nn.LogSoftmax(dim=1))
]))
model.classifier = classifier
model.load_state_dict(torch.load('vgg_test.pth'))

If you comment out the model.classifier = classifier line before loading the state_dict, you’ll get the same error you are describing.