def fwd_j2plus_rot(x, h0a, h1a, h0b, h1b, h2a, h2b, skip_hps, o_dim, mode):
""" Level 2 plus 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.
"""
if not skip_hps:
lo = rowdfilt(x, h0b, h0a, False, mode)
hi = rowdfilt(x, h1b, h1a, True, mode)
ba = rowdfilt(x, h2b, h2a, True, mode)
lh = coldfilt(lo, h1b, h1a, True, mode)
hl = coldfilt(hi, h0b, h0a, False, mode)
hh = coldfilt(ba, h2b, h2a, True, mode)
ll = coldfilt(lo, h0b, h0a, False, mode)
del lo, hi, ba
highr, highi = highs_to_orientations(lh, hl, hh, o_dim)
else:
ll = rowdfilt(x, h0b, h0a, False, mode)
ll = coldfilt(ll, h0b, h0a, False, mode)
highr = None
highi = None
return ll, highr, highi
def test_gradients():
ha = qshift('qshift_c')[0]
hb = qshift('qshift_c')[1]
im_t = torch.unsqueeze(torch.tensor(barbara, dtype=torch.float32,
requires_grad=True), dim=0)
y_t = coldfilt(im_t, prep_filt(ha, 1), prep_filt(hb, 1), np.sum(ha*hb) > 0)
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_qshift1(hp):
if hp:
ha = qshift('qshift_a')[4]
hb = qshift('qshift_a')[5]
else:
ha = qshift('qshift_a')[0]
hb = qshift('qshift_a')[1]
ref = ref_coldfilt(barbara, ha, hb)
y = coldfilt(barbara_t, prep_filt(ha, 1).to(dev), prep_filt(hb, 1).to(dev),
highpass=hp)
np.testing.assert_array_almost_equal(y[0].cpu(), ref, decimal=4)
def test_equal_small_in(hp):
if hp:
ha = qshift('qshift_a')[4]
hb = qshift('qshift_a')[5]
else:
ha = qshift('qshift_a')[0]
hb = qshift('qshift_a')[1]
im = barbara[:,0:4,0:4]
im_t = torch.unsqueeze(torch.tensor(im, dtype=torch.float32), dim=0).to(dev)
ref = ref_coldfilt(im, ha, hb)
y = coldfilt(im_t, prep_filt(ha, 1).to(dev), prep_filt(hb, 1).to(dev),
highpass=hp)
np.testing.assert_array_almost_equal(y[0].cpu(), ref, decimal=4)
def test_output_size():
ha = prep_filt((-1, 1), 1).to(dev)
hb = prep_filt((1, -1), 1).to(dev)
y_op = coldfilt(barbara_t, ha, hb)
assert list(y_op.shape)[1:] == bshape_half
def test_good_input_size_non_orthogonal():
ha = prep_filt((1, 1), 1).to(dev)
hb = prep_filt((1, -1), 1).to(dev)
coldfilt(barbara_t[:,:,:,:511], ha, hb)
def test_good_input_size():
ha = prep_filt((-1, 1), 1).to(dev)
hb = prep_filt((1, -1), 1).to(dev)
coldfilt(barbara_t[:,:,:,:511], ha, hb)
def test_bad_input_size():
with raises(ValueError):
ha = prep_filt((-1, 1), 1).to(dev)
hb = prep_filt((1, -1), 1).to(dev)
coldfilt(barbara_t[:,:,:511,:], ha, hb)
def test_different_size():
with raises(ValueError):
ha = prep_filt((-0.5,-1,2,0.5), 1).to(dev)
hb = prep_filt((-1,2,1), 1).to(dev)
coldfilt(barbara_t, ha, hb)
def test_odd_filter():
with raises(ValueError):
ha = prep_filt((-1,2,-1), 1).to(dev)
hb = prep_filt((-1,2,1), 1).to(dev)
coldfilt(barbara_t, ha, hb)
