In the code, ml-act converts z_src_ot and z_dst_ot to CPU before learning the transport map.
It uses these np primitives/functions:
- np.float64
- np.mean
- np.random.randn
- np.sum
- np.concatenate
Those primitives/functions have a torch equivalent, so why not just use torch?
np.float64 -> torch.float64 or torch.double
np.mean() -> torch.mean()
np.random.randn() -> torch.randn()
np.sum() -> torch.sum()
np.concatenate() -> torch.cat()
In the code, ml-act converts
z_src_otandz_dst_otto CPU before learning the transport map.It uses these np primitives/functions:
Those primitives/functions have a torch equivalent, so why not just use torch?
np.float64 -> torch.float64 or torch.double
np.mean() -> torch.mean()
np.random.randn() -> torch.randn()
np.sum() -> torch.sum()
np.concatenate() -> torch.cat()