def inv_j1(ll, highr, highi, g0, g1, o_dim, h_dim, w_dim, mode):
""" Level1 inverse dtcwt.
Have it as a separate function as can be used by the forward pass of the
inverse transform and the backward pass of the forward transform.
"""
if highr is None or highr.shape == torch.Size([]):
y = rowfilter(colfilter(ll, g0), g0)
else:
# Get the double sampled bandpass coefficients
lh, hl, hh = orientations_to_highs(highr, highi, o_dim)
if ll is None or ll.shape == torch.Size([]):
# Interpolate
hi = colfilter(hh, g1, mode) + colfilter(hl, g0, mode)
lo = colfilter(lh, g1, mode)
del lh, hh, hl
else:
# Possibly cut back some rows to make the ll match the highs
r, c = ll.shape[2:]
r1, c1 = highr.shape[h_dim], highr.shape[w_dim]
if r != r1 * 2:
ll = ll[:, :, 1:-1]
if c != c1 * 2:
ll = ll[:, :, :, 1:-1]
# Interpolate
hi = colfilter(hh, g1, mode) + colfilter(hl, g0, mode)
lo = colfilter(lh, g1, mode) + colfilter(ll, g0, mode)
del lh, hl, hh
y = rowfilter(hi, g1, mode) + rowfilter(lo, g0, mode)
return y
def fwd_j1_rot(x, h0, h1, h2, skip_hps, o_dim, mode):
""" Level 1 forward dtcwt.
Have it as a separate function as can be used by
the forward pass of the forward transform and the backward pass of the
inverse transform.
"""
# Level 1 forward (biorthogonal analysis filters)
if not skip_hps:
lo = rowfilter(x, h0, mode)
hi = rowfilter(x, h1, mode)
ba = rowfilter(x, h2, mode)
lh = colfilter(lo, h1, mode)
hl = colfilter(hi, h0, mode)
hh = colfilter(ba, h2, mode)
ll = colfilter(lo, h0, mode)
del lo, hi, ba
highr, highi = highs_to_orientations(lh, hl, hh, o_dim)
else:
ll = rowfilter(x, h0, mode)
ll = colfilter(ll, h0, mode)
highr = x.new_zeros([])
highi = x.new_zeros([])
return ll, highr, highi
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_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_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 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_even_size_batch():
zero_t = torch.zeros([1, *barbara.shape], dtype=torch.float32).to(dev)
h = [-1, 1]
y = rowfilter(zero_t, prep_filt(h, 1).to(dev))
assert list(y.shape)[1:] == bshape_extracol
assert not np.any(y.cpu().numpy()[:] != 0.0)
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
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
def test_even_size():
h = [-1, -1]
y_op = rowfilter(barbara_t, prep_filt(h, 1).to(dev))
assert list(y_op.shape)[1:] == bshape_extracol
def test_odd_size():
h = [-1, 2, -1]
y_op = rowfilter(barbara_t, prep_filt(h, 1).to(dev))
assert list(y_op.shape)[1:] == bshape
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)
