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View Code? Open in Web Editor NEWFluidNet re-written with ATen tensor lib
FluidNet re-written with ATen tensor lib
namespace at:: in cpp-extension will change in PyTorch v1.0 to namespace torch:: (Cpp API). This will have to be changed when updating to newer version
Hi,
I tried to run plume simulation with pre-trained model.
and I figured out I need config file for pre-trained model.
So, can you add config file for pre-trained model?
Or can I just use default trainConfig.yaml for pre-trained model?
Thank you for reading.
At execution, in the function Advect Scalar, called from advection.py input arguments behave as Variable
type (grad is computed) whereas intermediate objects in the function are just Tensor
type.
at::Tensor advectScalar
(
float dt, T src, T U, T flags,
const std::string method_str,
int bnd,
const bool sample_outside_fluid,
const float maccormack_strength
) {
// Size checking done in python side
int bsz = flags.size(0);
int d = flags.size(2);
int h = flags.size(3);
int w = flags.size(4);
bool is3D = (U.size(1) == 3);
src.set_requires_grad(false);
U.set_requires_grad(false);
flags.set_requires_grad(false);
T s_dst = zeros_like(src);
T fwd = zeros_like(src);
T bwd = zeros_like(src);
T fwd_pos = zeros_like(U);
T bwd_pos = zeros_like(U);
std::cout << "Is variable?" << std::endl;
std::cout << src.is_variable() << std::endl;
// returns 1, should be 0
std::cout << U.is_variable() << std::endl;
// returns 1, should be 0
std::cout << flags.is_variable() << std::endl;
// returns 1, should be 0
All should be of Tensor
type as no backpropagation is required in the advection part.
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