def __init__(self, J=1, wave='db1', mode='zero', separable=True):
super().__init__()
if isinstance(wave, str):
wave = pywt.Wavelet(wave)
if isinstance(wave, pywt.Wavelet):
h0_col, h1_col = wave.dec_lo, wave.dec_hi
h0_row, h1_row = h0_col, h1_col
else:
if len(wave) == 2:
h0_col, h1_col = wave[0], wave[1]
h0_row, h1_row = h0_col, h1_col
elif len(wave) == 4:
h0_col, h1_col = wave[0], wave[1]
h0_row, h1_row = wave[2], wave[3]
# Prepare the filters
if separable:
filts = lowlevel.prep_filt_afb2d(h0_col, h1_col, h0_row, h1_row)
self.h0_col = nn.Parameter(filts[0], requires_grad=False)
self.h1_col = nn.Parameter(filts[1], requires_grad=False)
self.h0_row = nn.Parameter(filts[2], requires_grad=False)
self.h1_row = nn.Parameter(filts[3], requires_grad=False)
self.h = (self.h0_col, self.h1_col, self.h0_row, self.h1_row)
else:
filts = lowlevel.prep_filt_afb2d_nonsep(h0_col, h1_col, h0_row, h1_row)
self.h = nn.Parameter(filts, requires_grad=False)
self.J = J
self.mode = mode
self.separable = separable
def __init__(self, J=1, wave='db1', mode='zero', separable=True):
super().__init__()
self.get_high_from_low = False
self.n_scales = 1
if isinstance(wave, str):
wave = pywt.Wavelet(wave)
if isinstance(wave, pywt.Wavelet):
h0_col, h1_col = np.expand_dims(np.array(wave.dec_lo),
0), np.expand_dims(
np.array(wave.dec_hi), 0)
h0_row, h1_row = np.expand_dims(np.array(h0_col),
0), np.expand_dims(
np.array(h1_col), 0)
else:
self.n_scales = len(wave[0])
if len(wave) == 1:
self.get_high_from_low = True
h0_col, h1_col = wave[0], None
h0_row, h1_row = None, None
if len(wave) == 2:
h0_col, h1_col = wave[0], wave[1]
h0_row, h1_row = None, None
elif len(wave) == 4:
h0_col, h1_col = wave[0], wave[1]
h0_row, h1_row = wave[2], wave[3]
# Prepare the filters
if separable:
filts = lowlevel.prep_filt_afb2d(h0_col, h1_col, h0_row, h1_row)
self.h0_col = filts[0]
self.h1_col = filts[1]
self.h0_row = filts[2]
self.h1_row = filts[3]
else:
filts, self.h0_col, self.h1_col, self.h0_row, self.h1_row = lowlevel.prep_filt_afb2d_nonsep(
h0_col, h1_col, h0_row, h1_row)
self.h = filts
self.J = J
self.mode = mode
self.separable = separable
def __init__(self, J=1, wave='db1', mode='zero'):
super().__init__()
if isinstance(wave, str):
wave = pywt.Wavelet(wave)
if isinstance(wave, pywt.Wavelet):
h0_col, h1_col = wave.dec_lo, wave.dec_hi
h0_row, h1_row = h0_col, h1_col
else:
if len(wave) == 2:
h0_col, h1_col = wave[0], wave[1]
h0_row, h1_row = h0_col, h1_col
elif len(wave) == 4:
h0_col, h1_col = wave[0], wave[1]
h0_row, h1_row = wave[2], wave[3]
# Prepare the filters
filts = lowlevel.prep_filt_afb2d(h0_col, h1_col, h0_row, h1_row)
self.register_buffer('h0_col', filts[0])
self.register_buffer('h1_col', filts[1])
self.register_buffer('h0_row', filts[2])
self.register_buffer('h1_row', filts[3])
self.J = J
self.mode = mode
def __init__(self, J=1, wave='db1', mode='periodization'):
super().__init__()
if isinstance(wave, str):
wave = pywt.Wavelet(wave)
if isinstance(wave, pywt.Wavelet):
h0_col, h1_col = wave.dec_lo, wave.dec_hi
h0_row, h1_row = h0_col, h1_col
else:
if len(wave) == 2:
h0_col, h1_col = wave[0], wave[1]
h0_row, h1_row = h0_col, h1_col
elif len(wave) == 4:
h0_col, h1_col = wave[0], wave[1]
h0_row, h1_row = wave[2], wave[3]
# Prepare the filters
filts = lowlevel.prep_filt_afb2d(h0_col, h1_col, h0_row, h1_row)
self.h0_col = nn.Parameter(filts[0], requires_grad=False)
self.h1_col = nn.Parameter(filts[1], requires_grad=False)
self.h0_row = nn.Parameter(filts[2], requires_grad=False)
self.h1_row = nn.Parameter(filts[3], requires_grad=False)
self.J = J
self.mode = mode
def __init__(self, decomp_level=1, wave="db1", padding_method="zero", device="cpu"):
super().__init__()
if isinstance(wave, str):
wave = pywt.Wavelet(wave)
if isinstance(wave, pywt.Wavelet):
h0_col, h1_col = wave.dec_lo, wave.dec_hi
h0_row, h1_row = h0_col, h1_col
else:
if len(wave) == 2:
h0_col, h1_col = wave[0], wave[1]
h0_row, h1_row = h0_col, h1_col
elif len(wave) == 4:
h0_col, h1_col = wave[0], wave[1]
h0_row, h1_row = wave[2], wave[3]
# Prepare the filters
filts = lowlevel.prep_filt_afb2d(h0_col, h1_col, h0_row, h1_row, device=device)
self.register_buffer("h0_col", filts[0])
self.register_buffer("h1_col", filts[1])
self.register_buffer("h0_row", filts[2])
self.register_buffer("h1_row", filts[3])
# todo: register the buffer for the output
self.decomp_level = decomp_level
self.padding_method = padding_method
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
