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import numpy as np
from Transform2d_np import Transform2d as Transform2d_np
from pytorch_wavelets import DTCWTForward, DTCWTInverse
from pytorch_wavelets.dtcwt.coeffs import biort as _biort, qshift as _qshift
import datasets
import torch
import py3nvml
from contextlib import contextmanager
PRECISION_FLOAT = 3
PRECISION_DOUBLE = 7
HAVE_GPU = torch.cuda.is_available()
if HAVE_GPU:
dev = torch.device('cuda')
else:
dev = torch.device('cpu')
@contextmanager
def set_double_precision():
old_prec = torch.get_default_dtype()
try:
torch.set_default_dtype(torch.float64)
yield
finally:
torch.set_default_dtype(old_prec)
def setup():
global barbara, barbara_t
global bshape, bshape_half
global ch
py3nvml.grab_gpus(1, gpu_fraction=0.5, env_set_ok=True)
barbara = datasets.barbara()
barbara = (barbara/barbara.max()).astype('float32')
barbara = barbara.transpose([2, 0, 1])
bshape = list(barbara.shape)
bshape_half = bshape[:]
bshape_half[1] //= 2
barbara_t = torch.unsqueeze(
torch.tensor(barbara, dtype=torch.float32, device=dev), dim=0)
ch = barbara_t.shape[1]
def test_barbara_loaded():
assert barbara.shape == (3, 512, 512)
assert barbara.min() >= 0
assert barbara.max() <= 1
assert barbara.dtype == np.float32
assert list(barbara_t.shape) == [1, 3, 512, 512]
def test_simple():
xfm = DTCWTForward(J=3).to(dev)
Yl, Yh = xfm(barbara_t)
assert len(Yl.shape) == 4
assert len(Yh) == 3
assert Yh[0].shape[-1] == 2
def test_specific_wavelet():
xfm = DTCWTForward(J=3, biort='antonini', qshift='qshift_06').to(dev)
Yl, Yh = xfm(barbara_t)
assert len(Yl.shape) == 4
assert len(Yh) == 3
assert Yh[0].shape[-1] == 2
def test_odd_rows():
xfm = DTCWTForward(J=3).to(dev)
Yl, Yh = xfm(barbara_t[:,:,:509])
def test_odd_cols():
xfm = DTCWTForward(J=3).to(dev)
Yl, Yh = xfm(barbara_t[:,:,:,:509])
def test_odd_rows_and_cols():
xfm = DTCWTForward(J=3).to(dev)
Yl, Yh = xfm(barbara_t[:,:,:509,:509])
@pytest.mark.parametrize("J, o_dim", [
(1, 2), (1, 1),(2, 2), (2, 3),
(3, 2), (3, 1),(4, 4), (4, 3),
(5, 2), (5, 1)
])
def test_fwd(J, o_dim):
X = 100*np.random.randn(3, 5, 100, 100)
Xt = torch.tensor(X, dtype=torch.get_default_dtype(), device=dev)
xfm = DTCWTForward(J=J, o_dim=o_dim).to(dev)
Yl, Yh = xfm(Xt)
f1 = Transform2d_np()
yl, yh = f1.forward(X, nlevels=J)
np.testing.assert_array_almost_equal(
Yl.cpu(), yl, decimal=PRECISION_FLOAT)
for i in range(len(yh)):
for l in range(6):
ours_r = np.take(Yh[i][...,0].cpu().numpy(), l, o_dim)
ours_i = np.take(Yh[i][...,1].cpu().numpy(), l, o_dim)
np.testing.assert_array_almost_equal(
ours_r, yh[i][:,:,l].real, decimal=PRECISION_FLOAT)
np.testing.assert_array_almost_equal(
ours_i, yh[i][:,:,l].imag, decimal=PRECISION_FLOAT)
@pytest.mark.parametrize("J, o_dim", [
(1, 2), (1, 1),(2, 2), (2, 3),
(3, 2), (3, 1),(4, 4), (4, 3),
(5, 2), (5, 1)
])
def test_fwd_double(J, o_dim):
with set_double_precision():
X = 100*np.random.randn(3, 5, 100, 100)
Xt = torch.tensor(X, dtype=torch.get_default_dtype(), device=dev)
xfm = DTCWTForward(J=J, o_dim=o_dim).to(dev)
Yl, Yh = xfm(Xt)
assert Yl.dtype == torch.float64
f1 = Transform2d_np()
yl, yh = f1.forward(X, nlevels=J)
np.testing.assert_array_almost_equal(
Yl.cpu(), yl, decimal=PRECISION_DOUBLE)
for i in range(len(yh)):
for l in range(6):
ours_r = np.take(Yh[i][...,0].cpu().numpy(), l, o_dim)
ours_i = np.take(Yh[i][...,1].cpu().numpy(), l, o_dim)
np.testing.assert_array_almost_equal(
ours_r, yh[i][:,:,l].real, decimal=PRECISION_DOUBLE)
np.testing.assert_array_almost_equal(
ours_i, yh[i][:,:,l].imag, decimal=PRECISION_DOUBLE)
@pytest.mark.parametrize("J, o_dim", [
(1, 2), (1, 1),(2, 2), (2, 3),
(3, 2), (3, 1),(4, 4), (4, 3),
(5, 2), (5, 1)
])
def test_fwd_skip_hps(J, o_dim):
X = 100*np.random.randn(3, 5, 100, 100)
# Randomly turn on/off the highpass outputs
hps = np.random.binomial(size=J, n=1,p=0.5).astype('bool')
xfm = DTCWTForward(J=J, skip_hps=hps, o_dim=o_dim).to(dev)
Yl, Yh = xfm(torch.tensor(X, dtype=torch.float32, device=dev))
f1 = Transform2d_np()
yl, yh = f1.forward(X, nlevels=J)
np.testing.assert_array_almost_equal(
Yl.cpu(), yl, decimal=PRECISION_FLOAT)
for j in range(J):
if hps[j]:
assert Yh[j].shape == torch.Size([])
else:
for l in range(6):
ours_r = np.take(Yh[j][...,0].cpu().numpy(), l, o_dim)
ours_i = np.take(Yh[j][...,1].cpu().numpy(), l, o_dim)
np.testing.assert_array_almost_equal(
ours_r, yh[j][:,:,l].real, decimal=PRECISION_FLOAT)
np.testing.assert_array_almost_equal(
ours_i, yh[j][:,:,l].imag, decimal=PRECISION_FLOAT)
@pytest.mark.parametrize("scales", [
(True,), (True, True), (True, True, True), (True, True, True, True),
(True, False, True), (False, True, True), (True, True, False),
(True, True, False, True)])
def test_fwd_include_scale(scales):
X = 100*np.random.randn(3, 5, 100, 100)
# Randomly turn on/off the highpass outputs
J = len(scales)
xfm = DTCWTForward(J=J, include_scale=scales).to(dev)
Ys, Yh = xfm(torch.tensor(X, dtype=torch.float32, device=dev))
f1 = Transform2d_np()
yl, yh, ys = f1.forward(X, nlevels=J, include_scale=True)
for j in range(J):
if not scales[j]:
assert Ys[j].shape == torch.Size([])
else:
np.testing.assert_array_almost_equal(
Ys[j].cpu(), ys[j], decimal=PRECISION_FLOAT)
@pytest.mark.parametrize("o_dim, ri_dim", [(2, -1), (1, -1), (1, 2), (2, 3),
(2, 1)])
def test_fwd_ri_dim(o_dim, ri_dim):
J = 3
X = 100*np.random.randn(3, 5, 100, 100)
Xt = torch.tensor(X, dtype=torch.get_default_dtype(), device=dev)
xfm = DTCWTForward(J=J, o_dim=o_dim, ri_dim=ri_dim).to(dev)
Yl, Yh = xfm(Xt)
f1 = Transform2d_np()
yl, yh = f1.forward(X, nlevels=J)
np.testing.assert_array_almost_equal(
Yl.cpu(), yl, decimal=PRECISION_FLOAT)
if (ri_dim % 6) < o_dim:
o_dim -= 1
for i in range(len(yh)):
ours_r = np.take(Yh[i].cpu().numpy(), 0, ri_dim)
ours_i = np.take(Yh[i].cpu().numpy(), 1, ri_dim)
for l in range(6):
ours = np.take(ours_r, l, o_dim)
np.testing.assert_array_almost_equal(
ours, yh[i][:,:,l].real, decimal=PRECISION_FLOAT)
ours = np.take(ours_i, l, o_dim)
np.testing.assert_array_almost_equal(
ours, yh[i][:,:,l].imag, decimal=PRECISION_FLOAT)
@pytest.mark.parametrize("scales", [
(True,), (True, True), (True, True, True), (True, True, True, True),
(True, False, True), (False, True, True), (True, True, False),
(True, True, False, True)])
def test_bwd_include_scale(scales):
X = 100*np.random.randn(3, 5, 100, 100)
# Randomly turn on/off the highpass outputs
J = len(scales)
xfm = DTCWTForward(J=J, include_scale=scales).to(dev)
Ys, Yh = xfm(torch.tensor(X, dtype=torch.float32, requires_grad=True,
device=dev))
f1 = Transform2d_np()
yl, yh, ys = f1.forward(X, nlevels=J, include_scale=True)
for ys in Ys:
if ys.requires_grad:
ys.backward(torch.ones_like(ys),retain_graph=True)
@pytest.mark.parametrize("J, o_dim", [
(1, 2), (1, 1),(2, 2), (2, 3),
(3, 2), (3, 1),(4, 4), (4, 3),
(5, 2), (5, 1)
])
def test_inv(J, o_dim):
Yl = 100*np.random.randn(3, 5, 64, 64)
Yhr = [[np.random.randn(3, 5, 2**j, 2**j) for l in range(6)] for j in range(4+J,4,-1)]
Yhi = [[np.random.randn(3, 5, 2**j, 2**j) for l in range(6)] for j in range(4+J,4,-1)]
Yh1 = [np.stack(r, axis=2) + 1j*np.stack(i, axis=2) for r, i in zip(Yhr, Yhi)]
Yh2 = [np.stack((np.stack(r, axis=o_dim), np.stack(i, axis=o_dim)), axis=-1)
for r, i in zip(Yhr, Yhi)]
Yh2 = [torch.tensor(yh, dtype=torch.float32, device=dev) for yh in Yh2]
ifm = DTCWTInverse(o_dim=o_dim).to(dev)
X = ifm((torch.tensor(Yl, dtype=torch.float32, device=dev), Yh2))
f1 = Transform2d_np()
x = f1.inverse(Yl, Yh1)
np.testing.assert_array_almost_equal(
X.cpu(), x, decimal=PRECISION_FLOAT)
@pytest.mark.parametrize("J, o_dim", [
(1, 2), (1, 1),(2, 2), (2, 3),
(3, 2), (3, 1),(4, 4), (4, 3),
(5, 2), (5, 1)
])
def test_inv_skip_hps(J, o_dim):
hps = np.random.binomial(size=J, n=1,p=0.5).astype('bool')
Yl = 100*np.random.randn(3, 5, 64, 64)
Yhr = [[np.random.randn(3, 5, 2**j, 2**j) for l in range(6)] for j in range(4+J,4,-1)]
Yhi = [[np.random.randn(3, 5, 2**j, 2**j) for l in range(6)] for j in range(4+J,4,-1)]
Yh1 = [np.stack(r, axis=2) + 1j*np.stack(i, axis=2) for r, i in zip(Yhr, Yhi)]
Yh2 = [np.stack((np.stack(r, axis=o_dim), np.stack(i, axis=o_dim)), axis=-1)
for r, i in zip(Yhr, Yhi)]
Yh2 = [torch.tensor(yh, dtype=torch.float32, device=dev) for yh in Yh2]
for j in range(J):
if hps[j]:
Yh2[j] = torch.tensor(0.)
Yh1[j] = np.zeros_like(Yh1[j])
ifm = DTCWTInverse(o_dim=o_dim).to(dev)
X = ifm((torch.tensor(Yl, dtype=torch.float32, requires_grad=True,
device=dev), Yh2))
# Also test giving None instead of an empty tensor
for j in range(J):
if hps[j]:
Yh2[j] = None
X2 = ifm((torch.tensor(Yl, dtype=torch.float32, device=dev), Yh2))
f1 = Transform2d_np()
x = f1.inverse(Yl, Yh1)
np.testing.assert_array_almost_equal(
X.detach().cpu(), x, decimal=PRECISION_FLOAT)
np.testing.assert_array_almost_equal(
X2.cpu(), x, decimal=PRECISION_FLOAT)
# Test gradients are ok
X.backward(torch.ones_like(X))
@pytest.mark.parametrize("ri_dim", [-1, 1, 2, 3])
def test_inv_ri_dim(ri_dim):
Yl = 100*np.random.randn(3, 5, 64, 64)
J = 3
Yhr = [np.random.randn(3, 5, 6, 2**j, 2**j) for j in range(4+J,4,-1)]
Yhi = [np.random.randn(3, 5, 6, 2**j, 2**j) for j in range(4+J,4,-1)]
Yh1 = [yhr + 1j*yhi for yhr, yhi in zip(Yhr, Yhi)]
Yh2 = [torch.tensor(np.stack((yhr, yhi), axis=ri_dim),
dtype=torch.float32, device=dev)
for yhr, yhi in zip(Yhr, Yhi)]
if (ri_dim % 6) <= 2:
o_dim = 3
else:
o_dim = 2
ifm = DTCWTInverse(o_dim=o_dim, ri_dim=ri_dim).to(dev)
X = ifm((torch.tensor(Yl, dtype=torch.float32, device=dev), Yh2))
f1 = Transform2d_np()
x = f1.inverse(Yl, Yh1)
np.testing.assert_array_almost_equal(
X.cpu(), x, decimal=PRECISION_FLOAT)
# Test end to end with numpy inputs
@pytest.mark.parametrize("biort,qshift,size,J", [
('antonini','qshift_a', (128,128), 3),
('antonini','qshift_a', (126,126), 3),
('legall','qshift_a', (99,100), 4),
('near_sym_a','qshift_c', (104, 101), 2),
('near_sym_b','qshift_d', (126, 126), 3),
])
def test_end2end(biort, qshift, size, J):
im = np.random.randn(5,6,*size).astype('float32')
imt = torch.tensor(im, dtype=torch.float32, requires_grad=True, device=dev)
xfm = DTCWTForward(J=J, biort=biort, qshift=qshift).to(dev)
Yl, Yh = xfm(imt)
ifm = DTCWTInverse(biort=biort, qshift=qshift).to(dev)
y = ifm((Yl, Yh))
# Compare with numpy results
f_np = Transform2d_np(biort=biort, qshift=qshift)
yl, yh = f_np.forward(im, nlevels=J)
y2 = f_np.inverse(yl, yh)
np.testing.assert_array_almost_equal(y.detach().cpu(), y2, decimal=PRECISION_FLOAT)
# Test gradients are ok
y.backward(torch.ones_like(y))
# Test gradients
@pytest.mark.parametrize("biort,qshift,size,J", [
('antonini','qshift_a', (128,128), 3),
('antonini','qshift_a', (64,64), 3),
('legall','qshift_a', (240,240), 4),
('near_sym_a','qshift_c', (100, 100), 2),
('near_sym_b','qshift_d', (120, 120), 3),
])
def test_gradients_fwd(biort, qshift, size, J):
""" Gradient of forward function should be inverse function with filters
swapped """
im = np.random.randn(5,6,*size).astype('float32')
imt = torch.tensor(im, dtype=torch.float32, requires_grad=True, device=dev)
xfm = DTCWTForward(biort=biort, qshift=qshift, J=J).to(dev)
h0o, g0o, h1o, g1o = _biort(biort)
h0a, h0b, g0a, g0b, h1a, h1b, g1a, g1b = _qshift(qshift)
xfm_grad = DTCWTInverse(biort=(h0o[::-1], h1o[::-1]),
qshift=(h0a[::-1], h0b[::-1], h1a[::-1], h1b[::-1])
).to(dev)
Yl, Yh = xfm(imt)
Ylg = torch.randn(*Yl.shape, device=dev)
Yl.backward(Ylg, retain_graph=True)
ref = xfm_grad((Ylg, [None,]*J))
np.testing.assert_array_almost_equal(imt.grad.detach().cpu(), ref.cpu())
for j, y in enumerate(Yh):
imt.grad.zero_()
g = torch.randn(*y.shape, device=dev)
y.backward(g, retain_graph=True)
hps = [None,] * J
hps[j] = g
ref = xfm_grad((torch.zeros_like(Yl), hps))
np.testing.assert_array_almost_equal(imt.grad.detach().cpu(), ref.cpu())
@pytest.mark.parametrize("biort,qshift,size,J", [
('antonini','qshift_a', (128,128), 3),
('antonini','qshift_a', (64,64), 3),
('legall','qshift_a', (240,240), 4),
('near_sym_a','qshift_c', (100, 100), 2),
('near_sym_b','qshift_d', (120, 120), 3),
])
def test_gradients_inv(biort, qshift, size, J):
""" Gradient of forward function should be inverse function with filters
swapped """
im = np.random.randn(5,6,*size).astype('float32')
imt = torch.tensor(im, dtype=torch.float32, device=dev)
ifm = DTCWTInverse(biort=biort, qshift=qshift).to(dev)
h0o, g0o, h1o, g1o = _biort(biort)
h0a, h0b, g0a, g0b, h1a, h1b, g1a, g1b = _qshift(qshift)
ifm_grad = DTCWTForward(J=J, biort=(g0o[::-1], g1o[::-1]),
qshift=(g0a[::-1], g0b[::-1], g1a[::-1], g1b[::-1])
).to(dev)
yl, yh = ifm_grad(imt)
g = torch.randn(*imt.shape, device=dev)
ylv = torch.randn(*yl.shape, requires_grad=True, device=dev)
yhv = [torch.randn(*h.shape, requires_grad=True, device=dev) for h in yh]
Y = ifm((ylv, yhv))
Y.backward(g)
# Check the lowpass gradient is the same
ref_lp, ref_bp = ifm_grad(g)
np.testing.assert_array_almost_equal(ylv.grad.detach().cpu(), ref_lp.cpu())
# check the bandpasses are the same
for y, ref in zip(yhv, ref_bp):
np.testing.assert_array_almost_equal(y.grad.detach().cpu(), ref.cpu())
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