Save and load a Pytorch model

Question

i am trying to train a pytorch model on colab then save the model parameters and load it on my local computer.

After training, the model parameters are stored as below:

torch.save(Model.state_dict(),PATH)

loaded as below:

device = torch.device('cpu')
Model.load_state_dict(torch.load(PATH, map_location=device))

error:

AttributeError: 'Sequential' object has no attribute 'copy'

Does anyone know how to solve this issue?

Please update entire trace of the error message and would be easier if you could update question with the complete code for the ease of reproducing the error and troubleshoot — Saranraj Nambusubramaniyan, May 08 '19 at 13:30
did you try using `torch.save`? Why does that not meet your needs? — Charlie Parker, Jul 15 '20 at 19:59
just `torch.load` your model and then use it directly. See answer. — Charlie Parker, Jul 15 '20 at 20:26

score 0 · Answer 1 · answered Jul 15 '20 at 20:26

Your question does not provide sufficient details to be answered correctly. If you are trying to save and load your own model and have a class definition for it see this well known answer and clarify why that's not sufficient for your use.

If you are loading a torch.nn.Sequential model then as far as I know simply loading the model directly and just using it should be sufficient. If it's not post on the pytorch forum what error you get.

For now look at my example show casing loading a sequential model and then using it without error:


# test for saving everything with torch.save

import torch
import torch.nn as nn

from pathlib import Path
from collections import OrderedDict

import numpy as np

import pickle

path = Path('~/data/tmp/').expanduser()
path.mkdir(parents=True, exist_ok=True)

num_samples = 3
Din, Dout = 1, 1
lb, ub = -1, 1

x = torch.torch.distributions.Uniform(low=lb, high=ub).sample((num_samples, Din))

f = nn.Sequential(OrderedDict([
    ('f1', nn.Linear(Din,Dout)),
    ('out', nn.SELU())
]))
y = f(x)

# save data torch to numpy
x_np, y_np = x.detach().cpu().numpy(), y.detach().cpu().numpy()
db2 = {'f': f, 'x': x_np, 'y': y_np}
torch.save(db2, path / 'db_f_x_y')

db3 = torch.load(path / 'db_f_x_y')
f3 = db3['f']
x3 = db3['x']
y3 = db3['y']
xx = torch.tensor(x3)
yy3 = f3(xx)

print(yy3)

there should be an official answer how to save and load nn.Sequential models How does one save torch.nn.Sequential models in pytorch properly? but for now torch.save and torch.load seem to work just fine.

Save and load a Pytorch model

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