Esempio n. 1
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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
Esempio n. 2
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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(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
Esempio n. 5
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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
Esempio n. 6
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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