def test_3d_interp_backward():
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.requires_grad = True
y = kbinterp_ob.forward(x, ktraj)
((y**2) / 2).sum().backward()
x_grad = x.grad.clone().detach()
x_hat = adjkbinterp_ob.forward(y.clone().detach(), ktraj)
assert torch.norm(x_grad - x_hat) < norm_tol
def test_2d_interp_adjoint_backward():
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)
kbinterp_ob = KbInterpForw(im_size=(20, 25),
grid_size=im_size,
numpoints=(4, 6))
adjkbinterp_ob = KbInterpBack(im_size=(20, 25),
grid_size=im_size,
numpoints=(4, 6))
y.requires_grad = True
x = adjkbinterp_ob.forward(y, ktraj)
((x**2) / 2).sum().backward()
y_grad = y.grad.clone().detach()
y_hat = kbinterp_ob.forward(x.clone().detach(), ktraj)
assert torch.norm(y_grad - y_hat) < norm_tol
def test_interp_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)
kbinterp_ob = KbInterpForw(im_size=(20, 25),
grid_size=im_size,
numpoints=(4, 6))
adjkbinterp_ob = KbInterpBack(im_size=(20, 25),
grid_size=im_size,
numpoints=(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_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))
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_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_2d_interp_adjoint_backward(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)
real_mat, imag_mat = precomp_sparse_mats(ktraj, kbinterp_ob)
interp_mats = {
"real_interp_mats": real_mat,
"imag_interp_mats": imag_mat
}
y.requires_grad = True
x = adjkbinterp_ob.forward(y, ktraj, interp_mats)
((x**2) / 2).sum().backward()
y_grad = y.grad.clone().detach()
y_hat = kbinterp_ob.forward(x.clone().detach(), ktraj, interp_mats)
assert torch.norm(y_grad - y_hat) < norm_tol
def test_3d_interp_backward(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.requires_grad = True
y = kbinterp_ob.forward(x, ktraj, interp_mats)
((y**2) / 2).sum().backward()
x_grad = x.grad.clone().detach()
x_hat = adjkbinterp_ob.forward(y.clone().detach(), ktraj, interp_mats)
assert torch.norm(x_grad - x_hat) < norm_tol
def test_3d_interp_backward(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)
x.requires_grad = True
y = kbinterp_ob.forward(x, ktraj)
((y**2) / 2).sum().backward()
x_grad = x.grad.clone().detach()
x_hat = adjkbinterp_ob.forward(y.clone().detach(), ktraj)
assert torch.norm(x_grad - x_hat) < norm_tol
def test_3d_interp_adjoint_backward():
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))
real_mat, imag_mat = precomp_sparse_mats(ktraj, kbinterp_ob)
interp_mats = {'real_interp_mats': real_mat, 'imag_interp_mats': imag_mat}
y.requires_grad = True
x = adjkbinterp_ob.forward(y, ktraj, interp_mats)
((x**2) / 2).sum().backward()
y_grad = y.grad.clone().detach()
y_hat = kbinterp_ob.forward(x.clone().detach(), ktraj, interp_mats)
assert torch.norm(y_grad - y_hat) < 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_kb_matching(testing_tol):
norm_tol = testing_tol
def check_tables(table1, table2):
for ind, table in enumerate(table1):
assert np.linalg.norm(table - table2[ind]) < norm_tol
im_szs = [(256, 256), (10, 256, 256)]
kbwidths = [2.34, 5]
orders = [0, 2]
for kbwidth in kbwidths:
for order in orders:
for im_sz in im_szs:
smap = torch.randn(*((1,) + im_sz))
base_table = AdjKbNufft(
im_sz, order=order, kbwidth=kbwidth).table
cur_table = KbNufft(im_sz, order=order, kbwidth=kbwidth).table
check_tables(base_table, cur_table)
cur_table = KbInterpBack(
im_sz, order=order, kbwidth=kbwidth).table
check_tables(base_table, cur_table)
cur_table = KbInterpForw(
im_sz, order=order, kbwidth=kbwidth).table
check_tables(base_table, cur_table)
cur_table = MriSenseNufft(
smap, im_sz, order=order, kbwidth=kbwidth).table
check_tables(base_table, cur_table)
cur_table = AdjMriSenseNufft(
smap, im_sz, order=order, kbwidth=kbwidth).table
check_tables(base_table, cur_table)
def test_2d_init_inputs():
# all object initializations have assertions
# this should result in an error if any dimensions don't match
# test 2d scalar inputs
im_sz = (256, 256)
smap = torch.randn(*((1,) + im_sz))
grid_sz = (512, 512)
n_shift = (128, 128)
numpoints = 6
table_oversamp = 2 ** 10
kbwidth = 2.34
order = 0
norm = "None"
ob = KbInterpForw(
im_size=im_sz,
grid_size=grid_sz,
n_shift=n_shift,
numpoints=numpoints,
table_oversamp=table_oversamp,
kbwidth=kbwidth,
order=order,
)
ob = KbInterpBack(
im_size=im_sz,
grid_size=grid_sz,
n_shift=n_shift,
numpoints=numpoints,
table_oversamp=table_oversamp,
kbwidth=kbwidth,
order=order,
)
ob = KbNufft(
im_size=im_sz,
grid_size=grid_sz,
n_shift=n_shift,
numpoints=numpoints,
table_oversamp=table_oversamp,
kbwidth=kbwidth,
order=order,
norm=norm,
)
ob = AdjKbNufft(
im_size=im_sz,
grid_size=grid_sz,
n_shift=n_shift,
numpoints=numpoints,
table_oversamp=table_oversamp,
kbwidth=kbwidth,
order=order,
norm=norm,
)
ob = MriSenseNufft(
smap=smap,
im_size=im_sz,
grid_size=grid_sz,
n_shift=n_shift,
numpoints=numpoints,
table_oversamp=table_oversamp,
kbwidth=kbwidth,
order=order,
norm=norm,
)
ob = AdjMriSenseNufft(
smap=smap,
im_size=im_sz,
grid_size=grid_sz,
n_shift=n_shift,
numpoints=numpoints,
table_oversamp=table_oversamp,
kbwidth=kbwidth,
order=order,
norm=norm,
)
# test 2d tuple inputs
im_sz = (256, 256)
smap = torch.randn(*((1,) + im_sz))
grid_sz = (512, 512)
n_shift = (128, 128)
numpoints = (6, 6)
table_oversamp = (2 ** 10, 2 ** 10)
kbwidth = (2.34, 2.34)
order = (0, 0)
norm = "None"
ob = KbInterpForw(
im_size=im_sz,
grid_size=grid_sz,
n_shift=n_shift,
numpoints=numpoints,
table_oversamp=table_oversamp,
kbwidth=kbwidth,
order=order,
)
ob = KbInterpBack(
im_size=im_sz,
grid_size=grid_sz,
n_shift=n_shift,
numpoints=numpoints,
table_oversamp=table_oversamp,
kbwidth=kbwidth,
order=order,
)
ob = KbNufft(
im_size=im_sz,
grid_size=grid_sz,
n_shift=n_shift,
numpoints=numpoints,
table_oversamp=table_oversamp,
kbwidth=kbwidth,
order=order,
norm=norm,
)
ob = AdjKbNufft(
im_size=im_sz,
grid_size=grid_sz,
n_shift=n_shift,
numpoints=numpoints,
table_oversamp=table_oversamp,
kbwidth=kbwidth,
order=order,
norm=norm,
)
ob = MriSenseNufft(
smap=smap,
im_size=im_sz,
grid_size=grid_sz,
n_shift=n_shift,
numpoints=numpoints,
table_oversamp=table_oversamp,
kbwidth=kbwidth,
order=order,
norm=norm,
)
ob = AdjMriSenseNufft(
smap=smap,
im_size=im_sz,
grid_size=grid_sz,
n_shift=n_shift,
numpoints=numpoints,
table_oversamp=table_oversamp,
kbwidth=kbwidth,
order=order,
norm=norm,
)
