Model.load_state_dict() returns NonType

1

Hi there!

I successfully trained my model that was declared as:

model = ModelClass(...)
model = torch.nn.DataParallel(model, device_ids=[0, 1, 2])

During training, I saved my weights like this:

best_model = copy.deepcopy(model.state_dict())
torch.save(best_model, path)

And I could get my weights for test (it isn’t NoneType, I checked):

w = torch.load(path) # type(w) is <class 'collections.OrderedDict'>

But! When I try:

trained_model = model.load_state_dict(w)

I got NoneType! What has happened? Maybe it happens due to DataParallel? What should I do?

2

You don’t have to assign the trained_model to load_state_dict. model should have all weights loaded.
Have a look at the Serialization info.

3 Likes
3

As far as I understood, all weights will be downloaded to model properly, right?

4

Yes, you could also verify it by printing some weights (print(model.layer_name.weight)) before and after loading.

1 Like
7

You may try the procedure:
define model=some_model(), and device=torch.device(‘cuda: 0,1,2’),
and model=torch.nn.DataParallel(model, device_ids=[0,1,2]), model=model.to(device),
then load the pretrained model:
model.module.load_state_dict(torch.load(‘path/to/model.pth’)

9

as mentioned in the answer, you do not need to assign the model to return value of load_state_dict().

Use of the following is enough:

G.load_state_dict(torch.load('G.pth'))
...
img = G(z)
1 Like
10

oh my fault. It is solved. Thank you :slight_smile: .
edit: I deleted my question .