def __init__(self, wave='db1', mode='zero', separable=True):
super().__init__()
if isinstance(wave, str):
wave = pywt.Wavelet(wave)
if isinstance(wave, pywt.Wavelet):
g0_col, g1_col = wave.rec_lo, wave.rec_hi
g0_row, g1_row = g0_col, g1_col
else:
if len(wave) == 2:
g0_col, g1_col = wave[0], wave[1]
g0_row, g1_row = g0_col, g1_col
elif len(wave) == 4:
g0_col, g1_col = wave[0], wave[1]
g0_row, g1_row = wave[2], wave[3]
# Prepare the filters
if separable:
filts = lowlevel.prep_filt_sfb2d(g0_col, g1_col, g0_row, g1_row)
self.g0_col = nn.Parameter(filts[0], requires_grad=False)
self.g1_col = nn.Parameter(filts[1], requires_grad=False)
self.g0_row = nn.Parameter(filts[2], requires_grad=False)
self.g1_row = nn.Parameter(filts[3], requires_grad=False)
self.h = (self.g0_col, self.g1_col, self.g0_row, self.g1_row)
else:
filts = lowlevel.prep_filt_sfb2d_nonsep(g0_col, g1_col, g0_row, g1_row)
self.h = nn.Parameter(filts, requires_grad=False)
self.mode = mode
self.separable = separable
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, wave='db1', mode='zero'):
super().__init__()
if isinstance(wave, str):
wave = pywt.Wavelet(wave)
if isinstance(wave, pywt.Wavelet):
g0_col, g1_col = wave.rec_lo, wave.rec_hi
g0_row, g1_row = g0_col, g1_col
else:
if len(wave) == 2:
g0_col, g1_col = wave[0], wave[1]
g0_row, g1_row = g0_col, g1_col
elif len(wave) == 4:
g0_col, g1_col = wave[0], wave[1]
g0_row, g1_row = wave[2], wave[3]
# Prepare the filters
filts = lowlevel.prep_filt_sfb2d(g0_col, g1_col, g0_row, g1_row)
self.register_buffer('g0_col', filts[0])
self.register_buffer('g1_col', filts[1])
self.register_buffer('g0_row', filts[2])
self.register_buffer('g1_row', filts[3])
self.mode = mode
def __init__(self, wave="db1", padding_method="zero", device="cpu"):
super().__init__()
if isinstance(wave, str):
wave = pywt.Wavelet(wave)
if isinstance(wave, pywt.Wavelet):
g0_col, g1_col = wave.rec_lo, wave.rec_hi
g0_row, g1_row = g0_col, g1_col
else:
if len(wave) == 2:
g0_col, g1_col = wave[0], wave[1]
g0_row, g1_row = g0_col, g1_col
elif len(wave) == 4:
g0_col, g1_col = wave[0], wave[1]
g0_row, g1_row = wave[2], wave[3]
# Prepare the filters
filts = lowlevel.prep_filt_sfb2d(g0_col, g1_col, g0_row, g1_row, device=device)
self.register_buffer("g0_col", filts[0])
self.register_buffer("g1_col", filts[1])
self.register_buffer("g0_row", filts[2])
self.register_buffer("g1_row", filts[3])
self.padding_method = padding_method