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_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 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 __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, 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', 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,
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_numpy_biort2():
h = _biort('near_sym_b')[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].cpu(), ref, decimal=4)
def test_gradients():
h = _biort('near_sym_b')[0]
im_t = torch.unsqueeze(torch.tensor(barbara,
dtype=torch.float32,
requires_grad=True),
dim=0).to(dev)
y_t = rowfilter(im_t, prep_filt(h, 1).to(dev))
dy = np.random.randn(*tuple(y_t.shape)).astype('float32')
torch.autograd.grad(y_t, im_t, grad_outputs=torch.tensor(dy))
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 __init__(self, biort='near_sym_a', mode='symmetric', magbias=1e-2):
super().__init__()
self.biort = 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
h0o, _, h1o, _ = _biort(biort)
self.h0o = torch.nn.Parameter(prep_filt(h0o, 1), False)
self.h1o = torch.nn.Parameter(prep_filt(h1o, 1), False)
self.lp_pool = nn.AvgPool2d(2)
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 test_equal_numpy_biort1():
h = _biort('near_sym_b')[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_biort():
h = _biort('antonini')[0]
y_op = rowfilter(barbara_t, prep_filt(h, 1).to(dev))
assert list(y_op.shape)[1:] == bshape
