Torch tensors in R are pointers to Tensors allocated by LibTorch.
This has one major consequence for serialization. One cannot simply use
saveRDS
for serializing tensors, as you would save the
pointer but not the data itself. When reloading a tensor saved with
saveRDS
the pointer might have been deleted in LibTorch and
you would get wrong results.
To solve this problem, torch
implements specialized
functions for serializing tensors to the disk:

torch_save()
: to save tensors and models to the disk. 
torch_load()
: to load the models or tensors back to the session.
Please note that this format is still experimental and you shouldn’t use it for long term storage.
Saving tensors
You can save any object of type torch_tensor
to the disk
using:
x < torch_randn(10, 10)
torch_save(x, "tensor.pt")
x_ < torch_load("tensor.pt")
torch_allclose(x, x_)
#> [1] TRUE
Saving modules
The torch_save
and torch_load
functions
also work for nn_modules
objects.
When saving an nn_module
, all the object is serialized
including the model structure and it’s state.
module < nn_module(
"my_module",
initialize = function() {
self$fc1 < nn_linear(10, 10)
self$fc2 < nn_linear(10, 1)
},
forward = function(x) {
x %>%
self$fc1() %>%
self$fc2()
}
)
model < module()
torch_save(model, "model.pt")
model_ < torch_load("model.pt")
# input tensor
x < torch_randn(50, 10)
torch_allclose(model(x), model_(x))
#> [1] TRUE
Loading models saved in python
Currently the only way to load models from python is to rewrite the model architecture in R. All the parameter names must be identical.
You can then save the PyTorch model state_dict using:
torch.save(model, fpath, _use_new_zipfile_serialization=True)
You can then reload the state dict in R and reload it into the model with:
state_dict < load_state_dict(fpath)
model < Model()
model$load_state_dict(state_dict)
You can find working examples in torchvision
. For
example this
is what we do for the AlexNet model.
Saving optimizer state
You can save the state of optimizers so you can continue training from the exact same position.
In order to this we use the state_dict()
and
load_state_dict()
methods from the optimizer combined with
torch_save
:
model < nn_linear(1, 1)
opt < optim_adam(model$parameters)
train_x < torch_randn(100, 1)
train_y < torch_randn(100, 1)
loss < nnf_mse_loss(model(train_x), train_y)
loss$backward()
opt$step()
#> NULL
# Now let's save the optimizer state
tmp < tempfile()
torch_save(opt$state_dict(), tmp)
# And now let's create a new optimizer and load back
opt2 < optim_adam(model$parameters)
opt2$load_state_dict(torch_load(tmp))