Access Weight and Bias with nn::sequential - libtorch

Question

If I define std::vector<torch::nn::Linear> linear_layers; and fill this vector with some torch::nn::Linear objects, then I can access the weight and bias values by linear_layers[k].weight and linear_layers[k].bias. Same feature is available with other layer types, e.g., torch::nn::Conv2d.
If create my network using nn::sequential and then push back either of Linear or Conv2d I cannot access the weight and bias directly. Now, my question is how can I access the weight and bias values of each layer when I have used nn::sequential?
Thanks,
Afshin

Answer 1

Here is the soultion: [see the link https://discuss.pytorch.org/t/common-class-of-linear-conv-etc/39987/8 ]
include
using namespace torch;
using namespace torch::nn;
int main()
{
auto net = Sequential(Conv2d(1 /input channels/, 1 /output channels/, 2 /kernel size/),
Conv2d(1, 1, 2));
for (auto& p : net->named_parameters()) {
NoGradGuard no_grad;
// Access name.
std::cout << p.key() << std::endl;
// Access weigth and bias.
p.value().zero_(); // set all zero
std::cout << p.value() << std::endl;
}
return 0;
}
The layers of a sequential, have the following naming convention: ., e.g. see the console output
0.weight # name of the layer
(1,1,.,.) =
0 0
0 0
[ Variable[CPUFloatType]{1,1,2,2} ]
0.bias
0
[ Variable[CPUFloatType]{1} ]
1.weight
(1,1,.,.) =
0 0
0 0
[ Variable[CPUFloatType]{1,1,2,2} ]
1.bias
0
[ Variable[CPUFloatType]{1} ]