def __init__(self, biort='near_sym_a', qshift='qshift_a', mode='symmetric',
magbias=1e-2, combine_colour=False):
super().__init__()
self.biort = biort
self.qshift = biort
# Have to convert the string to an int as the grad checks don't work
# with string inputs
self.mode_str = mode
self.mode = mode_to_int(mode)
self.magbias = magbias
self.combine_colour = combine_colour
if biort == 'near_sym_b_bp':
assert qshift == 'qshift_b_bp'
self.bandpass_diag = True
h0o, _, h1o, _, h2o, _ = _biort(biort)
self.h0o = torch.nn.Parameter(prep_filt(h0o, 1), False)
self.h1o = torch.nn.Parameter(prep_filt(h1o, 1), False)
self.h2o = torch.nn.Parameter(prep_filt(h2o, 1), False)
h0a, h0b, _, _, h1a, h1b, _, _, h2a, h2b, _, _ = _qshift('qshift_b_bp')
self.h0a = torch.nn.Parameter(prep_filt(h0a, 1), False)
self.h0b = torch.nn.Parameter(prep_filt(h0b, 1), False)
self.h1a = torch.nn.Parameter(prep_filt(h1a, 1), False)
self.h1b = torch.nn.Parameter(prep_filt(h1b, 1), False)
self.h2a = torch.nn.Parameter(prep_filt(h2a, 1), False)
self.h2b = torch.nn.Parameter(prep_filt(h2b, 1), False)
else:
self.bandpass_diag = False
h0o, _, h1o, _ = _biort(biort)
self.h0o = torch.nn.Parameter(prep_filt(h0o, 1), False)
self.h1o = torch.nn.Parameter(prep_filt(h1o, 1), False)
h0a, h0b, _, _, h1a, h1b, _, _ = _qshift(qshift)
self.h0a = torch.nn.Parameter(prep_filt(h0a, 1), False)
self.h0b = torch.nn.Parameter(prep_filt(h0b, 1), False)
self.h1a = torch.nn.Parameter(prep_filt(h1a, 1), False)
self.h1b = torch.nn.Parameter(prep_filt(h1b, 1), False)
def test_gradients_fwd(biort, qshift, size, J):
""" Gradient of forward function should be inverse function with filters
swapped """
im = np.random.randn(5, 6, *size).astype('float32')
imt = torch.tensor(im, dtype=torch.float32, requires_grad=True, device=dev)
xfm = DTCWTForward(biort=biort, qshift=qshift, J=J).to(dev)
h0o, g0o, h1o, g1o = _biort(biort)
h0a, h0b, g0a, g0b, h1a, h1b, g1a, g1b = _qshift(qshift)
xfm_grad = DTCWTInverse(biort=(h0o[::-1], h1o[::-1]),
qshift=(h0a[::-1], h0b[::-1], h1a[::-1],
h1b[::-1])).to(dev)
Yl, Yh = xfm(imt)
Ylg = torch.randn(*Yl.shape, device=dev)
Yl.backward(Ylg, retain_graph=True)
ref = xfm_grad((Ylg, [
None,
] * J))
np.testing.assert_array_almost_equal(imt.grad.detach().cpu(), ref.cpu())
for j, y in enumerate(Yh):
imt.grad.zero_()
g = torch.randn(*y.shape, device=dev)
y.backward(g, retain_graph=True)
hps = [
None,
] * J
hps[j] = g
ref = xfm_grad((torch.zeros_like(Yl), hps))
np.testing.assert_array_almost_equal(imt.grad.detach().cpu(),
ref.cpu())
def test_gradients_inv(biort, qshift, size, J):
""" Gradient of forward function should be inverse function with filters
swapped """
im = np.random.randn(5, 6, *size).astype('float32')
imt = torch.tensor(im, dtype=torch.float32, device=dev)
ifm = DTCWTInverse(biort=biort, qshift=qshift).to(dev)
h0o, g0o, h1o, g1o = _biort(biort)
h0a, h0b, g0a, g0b, h1a, h1b, g1a, g1b = _qshift(qshift)
ifm_grad = DTCWTForward(J=J,
biort=(g0o[::-1], g1o[::-1]),
qshift=(g0a[::-1], g0b[::-1], g1a[::-1],
g1b[::-1])).to(dev)
yl, yh = ifm_grad(imt)
g = torch.randn(*imt.shape, device=dev)
ylv = torch.randn(*yl.shape, requires_grad=True, device=dev)
yhv = [torch.randn(*h.shape, requires_grad=True, device=dev) for h in yh]
Y = ifm((ylv, yhv))
Y.backward(g)
# Check the lowpass gradient is the same
ref_lp, ref_bp = ifm_grad(g)
np.testing.assert_array_almost_equal(ylv.grad.detach().cpu(), ref_lp.cpu())
# check the bandpasses are the same
for y, ref in zip(yhv, ref_bp):
np.testing.assert_array_almost_equal(y.grad.detach().cpu(), ref.cpu())
def __init__(self, biort='farras', qshift='qshift_a',
mode='symmetric'):
super().__init__()
self.biort = biort
self.qshift = qshift
if isinstance(biort, str):
biort = level1(biort)
assert len(biort) == 8
_, _, g0a1, g0b1, _, _, g1a1, g1b1 = biort
IWTaa1 = DWTInverse(wave=(g0a1, g1a1, g0a1, g1a1), mode=mode)
IWTab1 = DWTInverse(wave=(g0a1, g1a1, g0b1, g1b1), mode=mode)
IWTba1 = DWTInverse(wave=(g0b1, g1b1, g0a1, g1a1), mode=mode)
IWTbb1 = DWTInverse(wave=(g0b1, g1b1, g0b1, g1b1), mode=mode)
self.level1 = nn.ModuleList([IWTaa1, IWTab1, IWTba1, IWTbb1])
if isinstance(qshift, str):
qshift = _qshift(qshift)
assert len(qshift) == 8
_, _, g0a, g0b, _, _, g1a, g1b = qshift
IWTaa = DWTInverse(wave=(g0a, g1a, g0a, g1a), mode=mode)
IWTab = DWTInverse(wave=(g0a, g1a, g0b, g1b), mode=mode)
IWTba = DWTInverse(wave=(g0b, g1b, g0a, g1a), mode=mode)
IWTbb = DWTInverse(wave=(g0b, g1b, g0b, g1b), mode=mode)
self.level2 = nn.ModuleList([IWTaa, IWTab, IWTba, IWTbb])
def __init__(self, biort='near_sym_a', qshift='qshift_a', o_dim=2,
ri_dim=-1, mode='symmetric'):
super().__init__()
self.biort = biort
self.qshift = qshift
self.o_dim = o_dim
self.ri_dim = ri_dim
self.mode = mode
if isinstance(biort, str):
_, g0o, _, g1o = _biort(biort)
self.g0o = torch.nn.Parameter(prep_filt(g0o, 1), False)
self.g1o = torch.nn.Parameter(prep_filt(g1o, 1), False)
else:
self.g0o = torch.nn.Parameter(prep_filt(biort[0], 1), False)
self.g1o = torch.nn.Parameter(prep_filt(biort[1], 1), False)
if isinstance(qshift, str):
_, _, g0a, g0b, _, _, g1a, g1b = _qshift(qshift)
self.g0a = torch.nn.Parameter(prep_filt(g0a, 1), False)
self.g0b = torch.nn.Parameter(prep_filt(g0b, 1), False)
self.g1a = torch.nn.Parameter(prep_filt(g1a, 1), False)
self.g1b = torch.nn.Parameter(prep_filt(g1b, 1), False)
else:
self.g0a = torch.nn.Parameter(prep_filt(qshift[0], 1), False)
self.g0b = torch.nn.Parameter(prep_filt(qshift[1], 1), False)
self.g1a = torch.nn.Parameter(prep_filt(qshift[2], 1), False)
self.g1b = torch.nn.Parameter(prep_filt(qshift[3], 1), False)
def __init__(self,
biort='near_sym_a',
qshift='qshift_a',
J=3,
o_dim=2,
ri_dim=-1):
super().__init__()
self.biort = biort
self.qshift = qshift
self.o_dim = o_dim
self.ri_dim = ri_dim
self.J = J
if isinstance(biort, str):
_, g0o, _, g1o = _biort(biort)
self.g0o = torch.nn.Parameter(prep_filt(g0o, 1), False)
self.g1o = torch.nn.Parameter(prep_filt(g1o, 1), False)
else:
self.g0o = torch.nn.Parameter(prep_filt(biort[0], 1), False)
self.g1o = torch.nn.Parameter(prep_filt(biort[1], 1), False)
if isinstance(qshift, str):
_, _, g0a, g0b, _, _, g1a, g1b = _qshift(qshift)
self.g0a = torch.nn.Parameter(prep_filt(g0a, 1), False)
self.g0b = torch.nn.Parameter(prep_filt(g0b, 1), False)
self.g1a = torch.nn.Parameter(prep_filt(g1a, 1), False)
self.g1b = torch.nn.Parameter(prep_filt(g1b, 1), False)
else:
self.g0a = torch.nn.Parameter(prep_filt(qshift[0], 1), False)
self.g0b = torch.nn.Parameter(prep_filt(qshift[1], 1), False)
self.g1a = torch.nn.Parameter(prep_filt(qshift[2], 1), False)
self.g1b = torch.nn.Parameter(prep_filt(qshift[3], 1), False)
# Create the function to do the DTCWT
self.dtcwt_func = getattr(tf, 'ifm{J}'.format(J=J))
def __init__(self,
biort='farras',
qshift='qshift_a',
J=3,
mode='symmetric'):
super().__init__()
self.biort = biort
self.qshift = qshift
self.J = J
if isinstance(biort, str):
biort = _level1(biort)
assert len(biort) == 8
h0a1, h0b1, _, _, h1a1, h1b1, _, _ = biort
DWTaa1 = DWTForward(J=1, wave=(h0a1, h1a1, h0a1, h1a1), mode=mode)
DWTab1 = DWTForward(J=1, wave=(h0a1, h1a1, h0b1, h1b1), mode=mode)
DWTba1 = DWTForward(J=1, wave=(h0b1, h1b1, h0a1, h1a1), mode=mode)
DWTbb1 = DWTForward(J=1, wave=(h0b1, h1b1, h0b1, h1b1), mode=mode)
self.level1 = nn.ModuleList([DWTaa1, DWTab1, DWTba1, DWTbb1])
if J > 1:
if isinstance(qshift, str):
qshift = _qshift(qshift)
assert len(qshift) == 8
h0a, h0b, _, _, h1a, h1b, _, _ = qshift
DWTaa = DWTForward(J - 1, (h0a, h1a, h0a, h1a), mode=mode)
DWTab = DWTForward(J - 1, (h0a, h1a, h0b, h1b), mode=mode)
DWTba = DWTForward(J - 1, (h0b, h1b, h0a, h1a), mode=mode)
DWTbb = DWTForward(J - 1, (h0b, h1b, h0b, h1b), mode=mode)
self.level2 = nn.ModuleList([DWTaa, DWTab, DWTba, DWTbb])
def __init__(self, C=None, biort='near_sym_a', qshift='qshift_a', J=3,
o_before_c=False):
super().__init__()
if C is not None:
warnings.warn('C parameter is deprecated. do not need to pass it')
self.biort = biort
self.qshift = qshift
self.o_before_c = o_before_c
self.J = J
if isinstance(biort, str):
_, g0o, _, g1o = _biort(biort)
self.g0o = torch.nn.Parameter(prep_filt(g0o, 1), False)
self.g1o = torch.nn.Parameter(prep_filt(g1o, 1), False)
else:
self.g0o = torch.nn.Parameter(prep_filt(biort[0], 1), False)
self.g1o = torch.nn.Parameter(prep_filt(biort[1], 1), False)
if isinstance(qshift, str):
_, _, g0a, g0b, _, _, g1a, g1b = _qshift(qshift)
self.g0a = torch.nn.Parameter(prep_filt(g0a, 1), False)
self.g0b = torch.nn.Parameter(prep_filt(g0b, 1), False)
self.g1a = torch.nn.Parameter(prep_filt(g1a, 1), False)
self.g1b = torch.nn.Parameter(prep_filt(g1b, 1), False)
else:
self.g0a = torch.nn.Parameter(prep_filt(qshift[0], 1), False)
self.g0b = torch.nn.Parameter(prep_filt(qshift[1], 1), False)
self.g1a = torch.nn.Parameter(prep_filt(qshift[2], 1), False)
self.g1b = torch.nn.Parameter(prep_filt(qshift[3], 1), False)
# Create the function to do the DTCWT
self.dtcwt_func = getattr(tf, 'ifm{J}'.format(J=J))
def __init__(self,
biort='near_sym_a',
qshift='qshift_a',
o_dim=2,
ri_dim=-1,
mode='symmetric'):
super().__init__()
self.biort = biort
self.qshift = qshift
self.o_dim = o_dim
self.ri_dim = ri_dim
self.mode = mode
if isinstance(biort, str):
_, g0o, _, g1o = _biort(biort)
self.register_buffer('g0o', prep_filt(g0o, 1))
self.register_buffer('g1o', prep_filt(g1o, 1))
else:
self.register_buffer('g0o', prep_filt(biort[0], 1))
self.register_buffer('g1o', prep_filt(biort[1], 1))
if isinstance(qshift, str):
_, _, g0a, g0b, _, _, g1a, g1b = _qshift(qshift)
self.register_buffer('g0a', prep_filt(g0a, 1))
self.register_buffer('g0b', prep_filt(g0b, 1))
self.register_buffer('g1a', prep_filt(g1a, 1))
self.register_buffer('g1b', prep_filt(g1b, 1))
else:
self.register_buffer('g0a', prep_filt(qshift[0], 1))
self.register_buffer('g0b', prep_filt(qshift[1], 1))
self.register_buffer('g1a', prep_filt(qshift[2], 1))
self.register_buffer('g1b', prep_filt(qshift[3], 1))
def __init__(self,
biort='near_sym_a',
qshift='qshift_a',
J=3,
skip_hps=False,
include_scale=False,
o_dim=2,
ri_dim=-1,
mode='symmetric'):
super().__init__()
if o_dim == ri_dim:
raise ValueError("Orientations and real/imaginary parts must be "
"in different dimensions.")
self.biort = biort
self.qshift = qshift
self.J = J
self.o_dim = o_dim
self.ri_dim = ri_dim
self.mode = mode
if isinstance(biort, str):
h0o, _, h1o, _ = _biort(biort)
self.h0o = prep_filt(h0o, 1)
self.h1o = prep_filt(h1o, 1)
else:
self.h0o = prep_filt(biort[0], 1)
self.h1o = prep_filt(biort[1], 1)
if isinstance(qshift, str):
h0a, h0b, _, _, h1a, h1b, _, _ = _qshift(qshift)
self.h0a = prep_filt(h0a, 1)
self.h0b = prep_filt(h0b, 1)
self.h1a = prep_filt(h1a, 1)
self.h1b = prep_filt(h1b, 1)
else:
self.h0a = prep_filt(qshift[0], 1)
self.h0b = prep_filt(qshift[1], 1)
self.h1a = prep_filt(qshift[2], 1)
self.h1b = prep_filt(qshift[3], 1)
self.h0o = nn.Parameter(self.h0o, requires_grad=True)
self.h1o = nn.Parameter(self.h1o, requires_grad=True)
self.h0a = nn.Parameter(self.h0a, requires_grad=True)
self.h0b = nn.Parameter(self.h0b, requires_grad=True)
self.h1a = nn.Parameter(self.h1a, requires_grad=True)
self.h1b = nn.Parameter(self.h1b, requires_grad=True)
# Get the function to do the DTCWT
if isinstance(skip_hps, (list, tuple, ndarray)):
self.skip_hps = skip_hps
else:
self.skip_hps = [
skip_hps,
] * self.J
if isinstance(include_scale, (list, tuple, ndarray)):
self.include_scale = include_scale
else:
self.include_scale = [
include_scale,
] * self.J
def test_equal_small_in():
h = _qshift('qshift_b')[0]
im = barbara[:, 0:4, 0:4]
im_t = torch.unsqueeze(torch.tensor(im, dtype=torch.float32),
dim=0).to(dev)
ref = ref_rowfilter(im, h)
y = rowfilter(im_t, prep_filt(h, 1).to(dev))
np.testing.assert_array_almost_equal(y[0].cpu(), ref, decimal=4)
def test_equal_numpy_qshift2():
h = _qshift('qshift_c')[0]
im = barbara[:, 52:407, 30:401]
im_t = torch.unsqueeze(torch.tensor(im, dtype=torch.float32),
dim=0).to(dev)
ref = ref_rowfilter(im, h)
y = rowfilter(im_t, prep_filt(h, 1).to(dev))
np.testing.assert_array_almost_equal(y[0], ref, decimal=4)
def __init__(self,
biort='near_sym_a',
qshift='qshift_a',
J=3,
skip_hps=False,
include_scale=False,
downsample=False,
o_dim=2,
ri_dim=-1):
super().__init__()
if o_dim == ri_dim:
raise ValueError("Orientations and real/imaginary parts must be "
"in different dimensions.")
self.biort = biort
self.qshift = qshift
self.J = J
self.downsample = downsample
self.o_dim = o_dim
self.ri_dim = ri_dim
if isinstance(biort, str):
h0o, _, h1o, _ = _biort(biort)
self.h0o = torch.nn.Parameter(prep_filt(h0o, 1), False)
self.h1o = torch.nn.Parameter(prep_filt(h1o, 1), False)
else:
self.h0o = torch.nn.Parameter(prep_filt(biort[0], 1), False)
self.h1o = torch.nn.Parameter(prep_filt(biort[1], 1), False)
if isinstance(qshift, str):
h0a, h0b, _, _, h1a, h1b, _, _ = _qshift(qshift)
self.h0a = torch.nn.Parameter(prep_filt(h0a, 1), False)
self.h0b = torch.nn.Parameter(prep_filt(h0b, 1), False)
self.h1a = torch.nn.Parameter(prep_filt(h1a, 1), False)
self.h1b = torch.nn.Parameter(prep_filt(h1b, 1), False)
else:
self.h0a = torch.nn.Parameter(prep_filt(qshift[0], 1), False)
self.h0b = torch.nn.Parameter(prep_filt(qshift[1], 1), False)
self.h1a = torch.nn.Parameter(prep_filt(qshift[2], 1), False)
self.h1b = torch.nn.Parameter(prep_filt(qshift[3], 1), False)
# Get the function to do the DTCWT
if isinstance(skip_hps, (list, tuple, ndarray)):
self.skip_hps = skip_hps
else:
self.skip_hps = [
skip_hps,
] * self.J
if isinstance(include_scale, (list, tuple, ndarray)):
self.include_scale = include_scale
else:
self.include_scale = [
include_scale,
] * self.J
if True in self.include_scale:
self.dtcwt_func = getattr(tf, 'xfm{J}scale'.format(J=J))
else:
self.dtcwt_func = getattr(tf, 'xfm{J}'.format(J=J))
def icplxdual2D(yl, yh, level1='farras', qshift='qshift_a', mode='periodization'):
# Get the filters
_, _, g0a1, g0b1, _, _, g1a1, g1b1 = _level1(level1)
_, _, g0a, g0b, _, _, g1a, g1b = _qshift(qshift)
dev = yl[0][0].device
Faf = ((prep_filt_sfb2d(g0a1, g1a1, g0a1, g1a1, device=dev),
prep_filt_sfb2d(g0a1, g1a1, g0b1, g1b1, device=dev)),
(prep_filt_sfb2d(g0b1, g1b1, g0a1, g1a1, device=dev),
prep_filt_sfb2d(g0b1, g1b1, g0b1, g1b1, device=dev)))
af = ((prep_filt_sfb2d(g0a, g1a, g0a, g1a, device=dev),
prep_filt_sfb2d(g0a, g1a, g0b, g1b, device=dev)),
(prep_filt_sfb2d(g0b, g1b, g0a, g1a, device=dev),
prep_filt_sfb2d(g0b, g1b, g0b, g1b, device=dev)))
# Convert the highs back to subbands
J = len(yh)
w = [[[[None for i in range(3)] for j in range(2)] for k in range(2)] for l in range(J)]
for j in range(J):
w[j][0][0][0], w[j][1][1][0] = pm(yh[j][:,2,:,:,:,0],
yh[j][:,3,:,:,:,1])
w[j][0][1][0], w[j][1][0][0] = pm(yh[j][:,3,:,:,:,0],
yh[j][:,2,:,:,:,1])
w[j][0][0][1], w[j][1][1][1] = pm(yh[j][:,0,:,:,:,0],
yh[j][:,5,:,:,:,1])
w[j][0][1][1], w[j][1][0][1] = pm(yh[j][:,5,:,:,:,0],
yh[j][:,0,:,:,:,1])
w[j][0][0][2], w[j][1][1][2] = pm(yh[j][:,1,:,:,:,0],
yh[j][:,4,:,:,:,1])
w[j][0][1][2], w[j][1][0][2] = pm(yh[j][:,4,:,:,:,0],
yh[j][:,1,:,:,:,1])
w[j][0][0] = torch.stack(w[j][0][0], dim=2)
w[j][0][1] = torch.stack(w[j][0][1], dim=2)
w[j][1][0] = torch.stack(w[j][1][0], dim=2)
w[j][1][1] = torch.stack(w[j][1][1], dim=2)
y = None
for m in range(2):
for n in range(2):
lo = yl[m][n]
for j in range(J-1, 0, -1):
lo = sfb2d(lo, w[j][m][n], af[m][n], mode=mode)
lo = sfb2d(lo, w[0][m][n], Faf[m][n], mode=mode)
# Add to the output
if y is None:
y = lo
else:
y = y + lo
# Normalize
y = y/2
return y
def __init__(self, C=None, biort='near_sym_a', qshift='qshift_a',
J=3, skip_hps=False, o_before_c=False, include_scale=False,
downsample=False):
super().__init__()
if C is not None:
warnings.warn('C parameter is deprecated. do not need to pass it '
'anymore.')
self.biort = biort
self.qshift = qshift
self.o_before_c = o_before_c
self.J = J
self.downsample = downsample
if isinstance(biort, str):
h0o, _, h1o, _ = _biort(biort)
self.h0o = torch.nn.Parameter(prep_filt(h0o, 1), False)
self.h1o = torch.nn.Parameter(prep_filt(h1o, 1), False)
else:
self.h0o = torch.nn.Parameter(prep_filt(biort[0], 1), False)
self.h1o = torch.nn.Parameter(prep_filt(biort[1], 1), False)
if isinstance(qshift, str):
h0a, h0b, _, _, h1a, h1b, _, _ = _qshift(qshift)
self.h0a = torch.nn.Parameter(prep_filt(h0a, 1), False)
self.h0b = torch.nn.Parameter(prep_filt(h0b, 1), False)
self.h1a = torch.nn.Parameter(prep_filt(h1a, 1), False)
self.h1b = torch.nn.Parameter(prep_filt(h1b, 1), False)
else:
self.h0a = torch.nn.Parameter(prep_filt(qshift[0], 1), False)
self.h0b = torch.nn.Parameter(prep_filt(qshift[1], 1), False)
self.h1a = torch.nn.Parameter(prep_filt(qshift[2], 1), False)
self.h1b = torch.nn.Parameter(prep_filt(qshift[3], 1), False)
# Get the function to do the DTCWT
if isinstance(skip_hps, (list, tuple, ndarray)):
self.skip_hps = skip_hps
else:
self.skip_hps = [skip_hps,] * self.J
if isinstance(include_scale, (list, tuple, ndarray)):
self.include_scale = include_scale
else:
self.include_scale = [include_scale,] * self.J
if True in self.include_scale:
self.dtcwt_func = getattr(tf, 'xfm{J}scale'.format(J=J))
else:
self.dtcwt_func = getattr(tf, 'xfm{J}'.format(J=J))
def cplxdual2D(x,
J,
level1='farras',
qshift='qshift_a',
mode='periodization',
mag=False):
""" Do a complex dtcwt
Returns:
lows: lowpass outputs from each of the 4 trees. Is a 2x2 list of lists
w: bandpass outputs from each of the 4 trees. Is a list of lists, with
shape [J][2][2][3]. Initially the 3 outputs are the lh, hl and hh from
each of the 4 trees. After doing sums and differences though, they
become the real and imaginary parts for the 6 orientations. In
particular:
first index - indexes over scales
second index - 0 = real, 1 = imaginary
third and fourth indices:
0,1 = 15 degrees
1,2 = 45 degrees
0,0 = 75 degrees
1,0 = 105 degrees
0,2 = 135 degrees
1,1 = 165 degrees
"""
x = x / 2
# Get the filters
h0a1, h0b1, _, _, h1a1, h1b1, _, _ = _level1(level1)
h0a, h0b, _, _, h1a, h1b, _, _ = _qshift(qshift)
Faf = ((prep_filt_afb2d(h0a1, h1a1, h0a1, h1a1, device=x.device),
prep_filt_afb2d(h0a1, h1a1, h0b1, h1b1, device=x.device)),
(prep_filt_afb2d(h0b1, h1b1, h0a1, h1a1, device=x.device),
prep_filt_afb2d(h0b1, h1b1, h0b1, h1b1, device=x.device)))
af = ((prep_filt_afb2d(h0a, h1a, h0a, h1a, device=x.device),
prep_filt_afb2d(h0a, h1a, h0b, h1b, device=x.device)),
(prep_filt_afb2d(h0b, h1b, h0a, h1a, device=x.device),
prep_filt_afb2d(h0b, h1b, h0b, h1b, device=x.device)))
# Do 4 fully decimated dwts
w = [[[None for _ in range(2)] for _ in range(2)] for j in range(J)]
lows = [[None for _ in range(2)] for _ in range(2)]
for m in range(2):
for n in range(2):
# Do the first level transform with the first level filters
# ll, bands = afb2d(x, (Faf[m][0], Faf[m][1], Faf[n][0], Faf[n][1]), mode=mode)
bands = afb2d(x, Faf[m][n], mode=mode)
# Separate the low and bandpasses
s = bands.shape
bands = bands.reshape(s[0], -1, 4, s[-2], s[-1])
ll = bands[:, :, 0].contiguous()
w[0][m][n] = [bands[:, :, 2], bands[:, :, 1], bands[:, :, 3]]
# Do the second+ level transform with the second level filters
for j in range(1, J):
# ll, bands = afb2d(ll, (af[m][0], af[m][1], af[n][0], af[n][1]), mode=mode)
bands = afb2d(ll, af[m][n], mode=mode)
# Separate the low and bandpasses
s = bands.shape
bands = bands.reshape(s[0], -1, 4, s[-2], s[-1])
ll = bands[:, :, 0].contiguous()
w[j][m][n] = [bands[:, :, 2], bands[:, :, 1], bands[:, :, 3]]
lows[m][n] = ll
# Convert the quads into real and imaginary parts
yh = [
None,
] * J
for j in range(J):
deg75r, deg105i = pm(w[j][0][0][0], w[j][1][1][0])
deg105r, deg75i = pm(w[j][0][1][0], w[j][1][0][0])
deg15r, deg165i = pm(w[j][0][0][1], w[j][1][1][1])
deg165r, deg15i = pm(w[j][0][1][1], w[j][1][0][1])
deg135r, deg45i = pm(w[j][0][0][2], w[j][1][1][2])
deg45r, deg135i = pm(w[j][0][1][2], w[j][1][0][2])
yhr = torch.stack((deg15r, deg45r, deg75r, deg105r, deg135r, deg165r),
dim=1)
yhi = torch.stack((deg15i, deg45i, deg75i, deg105i, deg135i, deg165i),
dim=1)
if mag:
yh[j] = torch.sqrt(yhr**2 + yhi**2 + 0.01) - np.sqrt(0.01)
else:
yh[j] = torch.stack((yhr, yhi), dim=-1)
return lows, yh
def test_equal_numpy_qshift1():
h = _qshift('qshift_c')[0]
ref = ref_rowfilter(barbara, h)
y = rowfilter(barbara_t, prep_filt(h, 1).to(dev))
np.testing.assert_array_almost_equal(y[0].cpu(), ref, decimal=4)
def test_qshift():
h = _qshift('qshift_a')[0]
x = barbara_t
y_op = rowfilter(x, prep_filt(h, 1).to(dev))
assert list(y_op.shape)[1:] == bshape_extracol
