def __init__(self,
diag,
dims=None,
dir=0,
device='cpu',
togpu=(False, False),
tocpu=(False, False),
dtype=torch.float32):
if not isinstance(diag, (torch.Tensor, ComplexTensor)):
self.complex = True if np.iscomplexobj(diag) else False
self.diag = \
complextorch_fromnumpy(diag.flatten()) if self.complex \
else torch.from_numpy(diag.flatten())
else:
self.complex = True if isinstance(diag, ComplexTensor) else False
self.diag = flatten(diag) if self.complex else diag.flatten()
if dims is None:
self.shape = (len(self.diag), len(self.diag))
self.dims = None
self.reshape = False
else:
diagdims = [1] * len(dims)
diagdims[dir] = dims[dir]
self.diag = reshape(self.diag, diagdims) if self.complex \
else self.diag.reshape(diagdims)
self.shape = (np.prod(dims), np.prod(dims))
self.dims = dims
self.reshape = True
self.device = device
self.togpu = togpu
self.tocpu = tocpu
self.dtype = dtype
self.explicit = False
self.Op = None
def _matvec(self, x):
if not self.reshape:
y = self.diag * x
else:
if self.complex:
x = reshape(x, self.dims)
y = flatten(self.diag * x)
else:
x = x.reshape(self.dims)
y = (self.diag * x).view(-1)
return y
def _rmatvec(self, x):
if self.complex:
diagadj = conj(self.diag)
else:
diagadj = self.diag
if not self.reshape:
y = diagadj * x
else:
if self.complex:
x = reshape(x, self.dims)
y = flatten(diagadj * x)
else:
x = x.reshape(self.dims)
y = (diagadj * x).view(-1)
return y
def _matvec(self, x):
if self.reshape:
x = reshape(x, self.newshape[0]) if self.complex else \
torch.reshape(x, self.newshape[0])
else:
if self.complex:
x = x.t()
if self.complex:
y = self.A.mm(x)
if not self.reshape:
y = y.t()
else:
y = self.A.matmul(x)
if self.reshape:
y = flatten(y) if self.complex else y.view(-1)
return y