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 __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 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_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_colfilter(im, h)
y = colfilter(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)
y_t = colfilter(im_t, prep_filt(h, 1))
dy = np.random.randn(*tuple(y_t.shape)).astype('float32')
torch.autograd.grad(y_t, im_t, grad_outputs=torch.tensor(dy))
def test_gradients(hp):
if hp:
ha = qshift('qshift_b')[4]
hb = qshift('qshift_b')[5]
else:
ha = qshift('qshift_b')[0]
hb = qshift('qshift_b')[1]
im_t = torch.unsqueeze(torch.tensor(barbara,
dtype=torch.float32,
requires_grad=True),
dim=0).to(dev)
y_t = rowdfilt(im_t,
prep_filt(ha, 1).to(dev),
prep_filt(hb, 1).to(dev),
highpass=hp)
np.random.randn(*tuple(y_t.shape)).astype('float32')
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_colfilter(im, h)
y = colfilter(im_t, prep_filt(h, 1).to(dev))
np.testing.assert_array_almost_equal(y[0].cpu(), ref, decimal=4)
def test_equal_numpy_qshift2():
h = _qshift('qshift_c')[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 __init__(self, biort='near_sym_a', mode='symmetric', magbias=1e-2,
combine_colour=False):
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
self.combine_colour = combine_colour
if biort == 'near_sym_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)
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)
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)
def test_qshift():
h = qshift('qshift_a')[0]
y_op = colfilter(barbara_t, prep_filt(h, 1).to(dev))
assert list(y_op.shape)[1:] == bshape_extrarow
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,
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 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_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_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_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_good_input_size_non_orthogonal():
ha = prep_filt((1, 1), 1).to(dev)
hb = prep_filt((1, -1), 1).to(dev)
rowdfilt(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)
rowdfilt(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)
rowdfilt(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)
rowdfilt(barbara_t, ha, hb)
def __init__(self,
biort='near_sym_a',
qshift='qshift_a',
mode='symmetric',
magbias=1e-2,
combine_colour=False,
J=2):
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
self.J = J
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_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 __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 = prep_filt(g0o, 1)
self.g1o = prep_filt(g1o, 1)
else:
self.g0o = prep_filt(biort[0], 1)
self.g1o = prep_filt(biort[1], 1)
if isinstance(qshift, str):
_, _, g0a, g0b, _, _, g1a, g1b = _qshift(qshift)
self.g0a = prep_filt(g0a, 1)
self.g0b = prep_filt(g0b, 1)
self.g1a = prep_filt(g1a, 1)
self.g1b = prep_filt(g1b, 1)
else:
self.g0a = prep_filt(qshift[0], 1)
self.g0b = prep_filt(qshift[1], 1)
self.g1a = prep_filt(qshift[2], 1)
self.g1b = prep_filt(qshift[3], 1)
self.g0o = nn.Parameter(self.g0o, requires_grad=True)
self.g1o = nn.Parameter(self.g1o, requires_grad=True)
self.g0a = nn.Parameter(self.g0a, requires_grad=True)
self.g0b = nn.Parameter(self.g0b, requires_grad=True)
self.g1a = nn.Parameter(self.g1a, requires_grad=True)
self.g1b = nn.Parameter(self.g1b, requires_grad=True)
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 __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_output_size():
ha = prep_filt((-1, 1), 1).to(dev)
hb = prep_filt((1, -1), 1).to(dev)
y_op = rowdfilt(barbara_t, ha, hb)
assert list(y_op.shape[1:]) == bshape_half
def test_even_size_batch():
zero_t = torch.zeros((1, *barbara.shape), dtype=torch.float32).to(dev)
y = colfilter(zero_t, prep_filt([-1, 1], 1).to(dev))
assert list(y.shape)[1:] == bshape_extrarow
assert not np.any(y.cpu().numpy()[:] != 0.0)
