def test_nufft_2d_adjoint():
dtype = torch.double
nslice = 2
ncoil = 4
im_size = (33, 24)
klength = 112
x = np.random.normal(size=(nslice, ncoil) + im_size) + \
1j*np.random.normal(size=(nslice, ncoil) + im_size)
x = torch.tensor(np.stack((np.real(x), np.imag(x)), axis=2)).to(dtype)
y = np.random.normal(size=(nslice, ncoil, klength)) + \
1j*np.random.normal(size=(nslice, ncoil, klength))
y = torch.tensor(np.stack((np.real(y), np.imag(y)), axis=2)).to(dtype)
ktraj = torch.randn(*(nslice, 2, klength)).to(dtype)
kbnufft_ob = KbNufft(im_size=im_size, numpoints=(4, 6))
adjkbnufft_ob = AdjKbNufft(im_size=im_size, numpoints=(4, 6))
x_forw = kbnufft_ob(x, ktraj)
y_back = adjkbnufft_ob(y, ktraj)
inprod1 = inner_product(y, x_forw, dim=2)
inprod2 = inner_product(y_back, x, dim=2)
assert torch.norm(inprod1 - inprod2) < norm_tol
def test_mrisensenufft_2d_adjoint():
dtype = torch.double
nslice = 2
ncoil = 4
im_size = (33, 24)
klength = 112
x = np.random.normal(size=(nslice, 1) + im_size) + \
1j*np.random.normal(size=(nslice, 1) + im_size)
x = torch.tensor(np.stack((np.real(x), np.imag(x)), axis=2)).to(dtype)
y = np.random.normal(size=(nslice, ncoil, klength)) + \
1j*np.random.normal(size=(nslice, ncoil, klength))
y = torch.tensor(np.stack((np.real(y), np.imag(y)), axis=2)).to(dtype)
ktraj = torch.randn(*(nslice, 2, klength)).to(dtype)
smap_sz = (nslice, ncoil, 2) + im_size
smap = torch.randn(*smap_sz).to(dtype)
sensenufft_ob = MriSenseNufft(smap=smap, im_size=im_size)
adjsensenufft_ob = AdjMriSenseNufft(smap=smap, im_size=im_size)
x_forw = sensenufft_ob(x, ktraj)
y_back = adjsensenufft_ob(y, ktraj)
inprod1 = inner_product(y, x_forw, dim=2)
inprod2 = inner_product(y_back, x, dim=2)
assert torch.norm(inprod1 - inprod2) < norm_tol
def test_toepnufft_2d_adjoint(params_2d, testing_tol, testing_dtype, device_list):
dtype = testing_dtype
norm_tol = testing_tol
im_size = params_2d["im_size"]
numpoints = params_2d["numpoints"]
x = params_2d["x"]
y = torch.randn(x.shape)
ktraj = params_2d["ktraj"]
for device in device_list:
x = x.detach().to(dtype=dtype, device=device)
y = y.detach().to(dtype=dtype, device=device)
ktraj = ktraj.detach().to(dtype=dtype, device=device)
adjkbnufft_ob = AdjKbNufft(im_size=im_size, numpoints=numpoints).to(
dtype=dtype, device=device
)
toep_ob = ToepNufft().to(dtype=dtype, device=device)
kern = calc_toep_kernel(adjkbnufft_ob, ktraj)
x_forw = toep_ob(x, kern)
y_back = toep_ob(y, kern)
inprod1 = inner_product(y, x_forw, dim=2)
inprod2 = inner_product(y_back, x, dim=2)
assert torch.norm(inprod1 - inprod2) < norm_tol
def test_interp_3d_adjoint():
dtype = torch.double
nslice = 2
ncoil = 4
im_size = (11, 33, 24)
klength = 112
x = np.random.normal(size=(nslice, ncoil) + im_size) + \
1j*np.random.normal(size=(nslice, ncoil) + im_size)
x = torch.tensor(np.stack((np.real(x), np.imag(x)), axis=2)).to(dtype)
y = np.random.normal(size=(nslice, ncoil, klength)) + \
1j*np.random.normal(size=(nslice, ncoil, klength))
y = torch.tensor(np.stack((np.real(y), np.imag(y)), axis=2)).to(dtype)
ktraj = torch.randn(*(nslice, 3, klength)).to(dtype)
kbinterp_ob = KbInterpForw(im_size=(5, 20, 25),
grid_size=im_size,
numpoints=(2, 4, 6))
adjkbinterp_ob = KbInterpBack(im_size=(5, 20, 25),
grid_size=im_size,
numpoints=(2, 4, 6))
x_forw = kbinterp_ob(x, ktraj)
y_back = adjkbinterp_ob(y, ktraj)
inprod1 = inner_product(y, x_forw, dim=2)
inprod2 = inner_product(y_back, x, dim=2)
assert torch.norm(inprod1 - inprod2) < norm_tol
def test_mrisensenufft_3d_coilpack_adjoint(
params_2d, testing_tol, testing_dtype, device_list
):
dtype = testing_dtype
norm_tol = testing_tol
im_size = params_2d["im_size"]
numpoints = params_2d["numpoints"]
x = params_2d["x"]
y = params_2d["y"]
ktraj = params_2d["ktraj"]
smap = params_2d["smap"]
for device in device_list:
x = x.detach().to(dtype=dtype, device=device)
y = y.detach().to(dtype=dtype, device=device)
ktraj = ktraj.detach().to(dtype=dtype, device=device)
sensenufft_ob = MriSenseNufft(
smap=smap, im_size=im_size, numpoints=numpoints, coilpack=True
).to(dtype=dtype, device=device)
adjsensenufft_ob = AdjMriSenseNufft(
smap=smap, im_size=im_size, numpoints=numpoints, coilpack=True
).to(dtype=dtype, device=device)
x_forw = sensenufft_ob(x, ktraj)
y_back = adjsensenufft_ob(y, ktraj)
inprod1 = inner_product(y, x_forw, dim=2)
inprod2 = inner_product(y_back, x, dim=2)
assert torch.norm(inprod1 - inprod2) < norm_tol
def test_nufft_2d_adjoint(params_2d, testing_tol, testing_dtype, device_list):
dtype = testing_dtype
norm_tol = testing_tol
im_size = params_2d["im_size"]
numpoints = params_2d["numpoints"]
x = params_2d["x"]
y = params_2d["y"]
ktraj = params_2d["ktraj"]
for device in device_list:
x = x.detach().to(dtype=dtype, device=device)
y = y.detach().to(dtype=dtype, device=device)
ktraj = ktraj.detach().to(dtype=dtype, device=device)
kbnufft_ob = KbNufft(im_size=im_size, numpoints=numpoints).to(
dtype=dtype, device=device
)
adjkbnufft_ob = AdjKbNufft(im_size=im_size, numpoints=numpoints).to(
dtype=dtype, device=device
)
x_forw = kbnufft_ob(x, ktraj)
y_back = adjkbnufft_ob(y, ktraj)
inprod1 = inner_product(y, x_forw, dim=2)
inprod2 = inner_product(y_back, x, dim=2)
assert torch.norm(inprod1 - inprod2) < norm_tol
def test_mrisensenufft_3d_adjoint():
dtype = torch.double
nslice = 2
ncoil = 4
im_size = (11, 33, 24)
klength = 112
x = np.random.normal(size=(nslice, 1) + im_size) + \
1j*np.random.normal(size=(nslice, 1) + im_size)
x = torch.tensor(np.stack((np.real(x), np.imag(x)), axis=2)).to(dtype)
y = np.random.normal(size=(nslice, ncoil, klength)) + \
1j*np.random.normal(size=(nslice, ncoil, klength))
y = torch.tensor(np.stack((np.real(y), np.imag(y)), axis=2)).to(dtype)
ktraj = torch.randn(*(nslice, 3, klength)).to(dtype)
smap_sz = (nslice, ncoil, 2) + im_size
smap = torch.randn(*smap_sz).to(dtype)
sensenufft_ob = MriSenseNufft(smap=smap, im_size=im_size)
adjsensenufft_ob = AdjMriSenseNufft(smap=smap, im_size=im_size)
real_mat, imag_mat = precomp_sparse_mats(ktraj, sensenufft_ob)
interp_mats = {'real_interp_mats': real_mat, 'imag_interp_mats': imag_mat}
x_forw = sensenufft_ob(x, ktraj, interp_mats)
y_back = adjsensenufft_ob(y, ktraj, interp_mats)
inprod1 = inner_product(y, x_forw, dim=2)
inprod2 = inner_product(y_back, x, dim=2)
assert torch.norm(inprod1 - inprod2) < norm_tol
def test_interp_2d_adjoint(params_2d, testing_tol, testing_dtype, device_list):
dtype = testing_dtype
norm_tol = testing_tol
batch_size = params_2d["batch_size"]
im_size = params_2d["im_size"]
grid_size = params_2d["grid_size"]
numpoints = params_2d["numpoints"]
x = np.random.normal(size=(batch_size, 1) + grid_size) + 1j * np.random.normal(
size=(batch_size, 1) + grid_size
)
x = torch.tensor(np.stack((np.real(x), np.imag(x)), axis=2))
y = params_2d["y"]
ktraj = params_2d["ktraj"]
for device in device_list:
x = x.detach().to(dtype=dtype, device=device)
y = y.detach().to(dtype=dtype, device=device)
ktraj = ktraj.detach().to(dtype=dtype, device=device)
kbinterp_ob = KbInterpForw(
im_size=im_size, grid_size=grid_size, numpoints=numpoints
).to(dtype=dtype, device=device)
adjkbinterp_ob = KbInterpBack(
im_size=im_size, grid_size=grid_size, numpoints=numpoints
).to(dtype=dtype, device=device)
x_forw = kbinterp_ob(x, ktraj)
y_back = adjkbinterp_ob(y, ktraj)
inprod1 = inner_product(y, x_forw, dim=2)
inprod2 = inner_product(y_back, x, dim=2)
assert torch.norm(inprod1 - inprod2) < norm_tol
def test_nufft_3d_adjoint():
dtype = torch.double
nslice = 2
ncoil = 4
im_size = (11, 33, 24)
klength = 112
x = np.random.normal(size=(nslice, ncoil) + im_size) + \
1j*np.random.normal(size=(nslice, ncoil) + im_size)
x = torch.tensor(np.stack((np.real(x), np.imag(x)), axis=2)).to(dtype)
y = np.random.normal(size=(nslice, ncoil, klength)) + \
1j*np.random.normal(size=(nslice, ncoil, klength))
y = torch.tensor(np.stack((np.real(y), np.imag(y)), axis=2)).to(dtype)
ktraj = torch.randn(*(nslice, 3, klength)).to(dtype)
kbnufft_ob = KbNufft(im_size=im_size, numpoints=(2, 4, 6))
adjkbnufft_ob = AdjKbNufft(im_size=im_size, numpoints=(2, 4, 6))
real_mat, imag_mat = precomp_sparse_mats(ktraj, kbnufft_ob)
interp_mats = {'real_interp_mats': real_mat, 'imag_interp_mats': imag_mat}
x_forw = kbnufft_ob(x, ktraj, interp_mats)
y_back = adjkbnufft_ob(y, ktraj, interp_mats)
inprod1 = inner_product(y, x_forw, dim=2)
inprod2 = inner_product(y_back, x, dim=2)
assert torch.norm(inprod1 - inprod2) < norm_tol
def test_mrisensenufft_3d_coilpack_adjoint(params_2d, testing_tol,
testing_dtype, device_list):
dtype = testing_dtype
norm_tol = testing_tol
im_size = params_2d['im_size']
numpoints = params_2d['numpoints']
x = params_2d['x']
y = params_2d['y']
ktraj = params_2d['ktraj']
smap = params_2d['smap']
for device in device_list:
x = x.detach().to(dtype=dtype, device=device)
y = y.detach().to(dtype=dtype, device=device)
ktraj = ktraj.detach().to(dtype=dtype, device=device)
sensenufft_ob = MriSenseNufft(smap=smap,
im_size=im_size,
numpoints=numpoints,
coilpack=True).to(dtype=dtype,
device=device)
adjsensenufft_ob = AdjMriSenseNufft(smap=smap,
im_size=im_size,
numpoints=numpoints,
coilpack=True).to(dtype=dtype,
device=device)
real_mat, imag_mat = precomp_sparse_mats(ktraj, sensenufft_ob)
interp_mats = {
'real_interp_mats': real_mat,
'imag_interp_mats': imag_mat
}
x_forw = sensenufft_ob(x, ktraj, interp_mats)
y_back = adjsensenufft_ob(y, ktraj, interp_mats)
inprod1 = inner_product(y, x_forw, dim=2)
inprod2 = inner_product(y_back, x, dim=2)
assert torch.norm(inprod1 - inprod2) < norm_tol
def test_interp_3d_adjoint(params_3d, testing_tol, testing_dtype, device_list):
dtype = testing_dtype
norm_tol = testing_tol
batch_size = params_3d['batch_size']
im_size = params_3d['im_size']
grid_size = params_3d['grid_size']
numpoints = params_3d['numpoints']
x = np.random.normal(size=(batch_size, 1) + grid_size) + \
1j*np.random.normal(size=(batch_size, 1) + grid_size)
x = torch.tensor(np.stack((np.real(x), np.imag(x)), axis=2))
y = params_3d['y']
ktraj = params_3d['ktraj']
for device in device_list:
x = x.detach().to(dtype=dtype, device=device)
y = y.detach().to(dtype=dtype, device=device)
ktraj = ktraj.detach().to(dtype=dtype, device=device)
kbinterp_ob = KbInterpForw(im_size=im_size,
grid_size=grid_size,
numpoints=numpoints).to(dtype=dtype,
device=device)
adjkbinterp_ob = KbInterpBack(im_size=im_size,
grid_size=grid_size,
numpoints=numpoints).to(dtype=dtype,
device=device)
real_mat, imag_mat = precomp_sparse_mats(ktraj, kbinterp_ob)
interp_mats = {
'real_interp_mats': real_mat,
'imag_interp_mats': imag_mat
}
x_forw = kbinterp_ob(x, ktraj, interp_mats)
y_back = adjkbinterp_ob(y, ktraj, interp_mats)
inprod1 = inner_product(y, x_forw, dim=2)
inprod2 = inner_product(y_back, x, dim=2)
assert torch.norm(inprod1 - inprod2) < norm_tol
def test_mrisensenufft_3d_adjoint(params_3d, testing_tol, testing_dtype,
device_list):
dtype = testing_dtype
norm_tol = testing_tol
im_size = params_3d["im_size"]
numpoints = params_3d["numpoints"]
x = params_3d["x"]
y = params_3d["y"]
ktraj = params_3d["ktraj"]
smap = params_3d["smap"]
for device in device_list:
x = x.detach().to(dtype=dtype, device=device)
y = y.detach().to(dtype=dtype, device=device)
ktraj = ktraj.detach().to(dtype=dtype, device=device)
sensenufft_ob = MriSenseNufft(smap=smap,
im_size=im_size,
numpoints=numpoints).to(dtype=dtype,
device=device)
adjsensenufft_ob = AdjMriSenseNufft(smap=smap,
im_size=im_size,
numpoints=numpoints).to(
dtype=dtype, device=device)
real_mat, imag_mat = precomp_sparse_mats(ktraj, sensenufft_ob)
interp_mats = {
"real_interp_mats": real_mat,
"imag_interp_mats": imag_mat
}
x_forw = sensenufft_ob(x, ktraj, interp_mats)
y_back = adjsensenufft_ob(y, ktraj, interp_mats)
inprod1 = inner_product(y, x_forw, dim=2)
inprod2 = inner_product(y_back, x, dim=2)
assert torch.norm(inprod1 - inprod2) < norm_tol
def test_nufft_2d_adjoint(params_2d, testing_tol, testing_dtype, device_list):
dtype = testing_dtype
norm_tol = testing_tol
im_size = params_2d['im_size']
numpoints = params_2d['numpoints']
x = params_2d['x']
y = params_2d['y']
ktraj = params_2d['ktraj']
for device in device_list:
x = x.detach().to(dtype=dtype, device=device)
y = y.detach().to(dtype=dtype, device=device)
ktraj = ktraj.detach().to(dtype=dtype, device=device)
kbnufft_ob = KbNufft(im_size=im_size,
numpoints=numpoints).to(dtype=dtype,
device=device)
adjkbnufft_ob = AdjKbNufft(im_size=im_size,
numpoints=numpoints).to(dtype=dtype,
device=device)
real_mat, imag_mat = precomp_sparse_mats(ktraj, kbnufft_ob)
interp_mats = {
'real_interp_mats': real_mat,
'imag_interp_mats': imag_mat
}
x_forw = kbnufft_ob(x, ktraj, interp_mats)
y_back = adjkbnufft_ob(y, ktraj, interp_mats)
inprod1 = inner_product(y, x_forw, dim=2)
inprod2 = inner_product(y_back, x, dim=2)
assert torch.norm(inprod1 - inprod2) < norm_tol
