Unofficial complex Tensor support for Pytorch
Treats first half of tensor as real, second as imaginary. A few arithmetic operations are implemented to emulate complex arithmetic. Supports gradients.
pip install pytorch-complex-tensorEasy import
from pytorch_complex_tensor import ComplexTensorInit tensor
# equivalent to:
# np.asarray([[1+3j, 1+3j, 1+3j], [2+4j, 2+4j, 2+4j]]).astype(np.complex64)
C = ComplexTensor([[1, 1, 1], [2, 2, 2], [3, 3, 3], [4, 4, 4]])
C.requires_grad = TruePretty printing
print(C)
# tensor([['(1.0+3.0j)' '(1.0+3.0j)' '(1.0+3.0j)'],
# ['(2.0+4.0j)' '(2.0+4.0j)' '(2.0+4.0j)']])handles absolute value properly for complex tensors
# complex absolute value implementation
print(C.abs())
# tensor([[3.1623, 3.1623, 3.1623],
# [4.4721, 4.4721, 4.4721]], grad_fn=<SqrtBackward>)prints correct sizing treating first half of matrix as real, second as imag
print(C.size())
# torch.Size([2, 3])multiplies both complex and real tensors
# show matrix multiply with real tensor
# also works with complex tensor
x = torch.Tensor([[3, 3], [4, 4], [2, 2]])
xy = C.mm(x)
print(xy)
# tensor([['(9.0+27.0j)' '(9.0+27.0j)'],
# ['(18.0+36.0j)' '(18.0+36.0j)']])reduce ops return ComplexScalar
xy = xy.sum()
# this is now a complex scalar (thin wrapper with .real, .imag)
print(type(xy))
# pytorch_complex_tensor.complex_scalar.ComplexScalar
print(xy)
# (54+126j)which can be used for gradients without breaking anything... (differentiates wrt the real part)
# calculate dxy / dC
# for complex scalars, grad is wrt the real part
xy.backward()
print(C.grad)
# tensor([['(6.0-0.0j)' '(8.0-0.0j)' '(4.0-0.0j)'],
# ['(6.0-0.0j)' '(8.0-0.0j)' '(4.0-0.0j)']])supports all section ops...
print(C[-1])
print(C[0, 0:-2, ...])
print(C[0, ..., 0])| Operation | complex tensor | real tensor | complex scalar | real scalar |
|---|---|---|---|---|
| addition | Y | Y | Y | Y |
| subtraction | Y | Y | Y | Y |
| multiply | Y | Y | Y | Y |
| mm | Y | Y | Y | Y |
| abs | Y | - | - | - |
| t | Y | - | - | - |
| grads | Y | Y | Y | Y |