ATen create tensor of the same type/options

1

What is the difference between .options() and .type():

Lets say

    std::vector<at::Tensor> forward(at::Tensor xy, int height, int width) {
        auto tmp  = at::empty({10, 4, height, width}, xy.type());
        return {tmp}
    }

v.s.

    std::vector<at::Tensor> forward(at::Tensor xy, int height, int width) {
        auto tmp  = at::empty({10, 4, height, width}, xy.options());
        return {tmp}
    }

Would the latter include not only CUDA/Cpu but also the device that xy is on? in that case which device will be used in the first case?

2 
Would the latter include not only CUDA/Cpu but also the device that xy is on?

Yes

in that case which device will be used in the first case?

The current CUDA device (if xy is a CUDA tensor). You can set the device with DeviceGuard in C++ or torch.cuda.set_device in Python.

1 Like
3

Thanks!

One more question:
If I want to create a tensor with the same options but with type int, is there something like

    std::vector<at::Tensor> forward(at::Tensor xy, int height, int width) {
        auto tmp  = at::empty({10, 4, height, width}, xy.options().as_int());
        return {tmp}
    }
4

This is the solution

    std::vector<at::Tensor> forward(at::Tensor xy, int height, int width) {
        auto tmp  = at::empty({10, 4, height, width}, xy.options().dtype(at::kInt));
        return {tmp}
    }