Load and save a model

1

I got a problem when I want to load my trained models Therefore I created me a simple example to find out what the problem of my save and load method is.

Here you can see the file where I save my model:

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable


class MeinNetz(nn.Module):
    def __init__(self):
        super(MeinNetz, self).__init__()
        self.lin1 = nn.Linear(10, 10)
        self.lin2 = nn.Linear(10, 10)

    def forward(self, x):
        x = F.relu(self.lin1(x))
        x = (self.lin2(x))
        return x

netz = MeinNetz()

input = Variable(torch.ones(1, 10))
torch.save(netz.state_dict(), './net.pth')
output = netz(input)
print(output)

And here is the file where I want to load my model:

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable


class MeinNetz(nn.Module):
    def __init__(self):
        super(MeinNetz, self).__init__()
        self.lin1 = nn.Linear(10, 10)
        self.lin2 = nn.Linear(10, 10)

    def forward(self, x):
        x = F.relu(self.lin1(x))
        x = (self.lin2(x))
        return x

netz2 = MeinNetz

netz2.load_state_dict(torch.load('./net.pth'))
input = Variable(torch.ones(1,10))
output = netz2(input)
print(output)

When I run the load file I get the following error :

load_state_dict() missing 1 required positional argument: 'state_dict'

What is the problem here?

Sry im stupid I missed brackets. Problem of the simple example is solved

2

How to define our model class if we are using Fastai’s ConvLearner object from a pretrained model? Can you provide me an example for this just as you did there for your question?
Thanks in advance.

Subash

3

Hi Hille!
I am unable to understand the bracket error.
I am facing the same problem when executing the following line of code:

model1= nn.Module
model1.load_state_dict(torch.load("model1_weights.pt"))

There is no mismatch as far as I understand in the number of brackets.
Could you please help me understand this?

Thank you.