I’m following the tutorial here, and after getting it to load the model I wanted to run it. I had to make some small adjustments to the example code as my input features are indices (and just on the side, I could not find any documentation for torch::ones, expected it to be here but didn’t find it, and trying to use torch::kInt64 resulted in “is not a member of ‘torch’” despite it being listed on that page?).
I got it to compile with this
#include <torch/script.h> // One-stop header.
#include <iostream>
#include <memory>
#include <vector>
int main(int argc, const char* argv[]) {
if (argc != 2) {
std::cerr << "usage: example-app <path-to-exported-script-module>\n";
return -1;
}
// Deserialize the ScriptModule from a file using torch::jit::load().
std::shared_ptr<torch::jit::script::Module> module = torch::jit::load(argv[1]);
assert(module != nullptr);
std::vector<torch::jit::IValue> inputs;
at::TensorOptions tens_opts;
tens_opts.dtype(at::kLong);
inputs.push_back(torch::ones({1, 2}, tens_opts));
auto output = module->forward(inputs).toTensor();
std::cout << output.slice(0) << std::endl;;
}
But when running it I get an error:
terminate called after throwing an instance of 'at::Error'
what(): isTensor() ASSERT FAILED at [...]/libtorch/include/ATen/core/ivalue.h:153, please report a bug to PyTorch. (toTensor at [...]/libtorch/include/ATen/core/ivalue.h:153)
I’m doing slicing in the forward definition of my model, could that be an issue? Here’s my model definition
class CRNN(nn.Module):
def __init__(self, num_inp, num_hid, num_ff, num_layers, num_out):
super(CRNN, self).__init__()
self.embed = nn.Embedding(num_inp, num_hid)
self.rnn = nn.GRU(num_hid, num_hid, num_layers, batch_first=True)
self.fc_emb_skip = nn.Linear(num_hid, num_ff)
self.fc1 = nn.Linear(num_hid + num_ff, num_ff)
self.fc2 = nn.Linear(num_ff, 3)
self.hidden_init = nn.Parameter(t.randn(num_layers, 1, num_hid).type(t.FloatTensor), requires_grad=True)
self.num_inp = num_inp
self.num_hid = num_hid
self.num_out = num_out
self.num_layers = num_layers
def forward(self, x):
hidden = self.init_hidden(x.size(0))
emb = self.embed(x)
output, hidden = self.rnn(emb, hidden)
y_skip = F.elu(self.fc_emb_skip(emb[:, :-1]))
joined_out = t.cat((y_skip, output[:, 1:],), dim=2)
outview = joined_out.contiguous().view(joined_out.size(0) * joined_out.size(1), joined_out.size(2))
y = F.elu(self.fc1(outview))
logprobs = F.log_softmax(self.fc2(y), 1)
return logprobs, hidden
def init_hidden(self, batch_size):
return self.hidden_init.repeat(1, batch_size, 1)