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29b9c56 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 | import numpy as np
import pytest
import pywt
from pytorch_wavelets import DWTForward, DWTInverse
import torch
from contextlib import contextmanager
PREC_FLT = 3
PREC_DBL = 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)
@pytest.mark.parametrize("wave, J, mode", [
('db1', 1, 'zero'),
('db1', 3, 'zero'),
('db3', 1, 'symmetric'),
('db3', 2, 'reflect'),
('db2', 3, 'periodization'),
('db2', 3, 'periodic'),
('db4', 2, 'zero'),
('db3', 3, 'symmetric'),
('bior2.4', 2, 'periodization'),
('bior2.4', 2, 'periodization'),
])
def test_ok(wave, J, mode):
x = torch.randn(5, 4, 64, 64).to(dev)
dwt = DWTForward(J=J, wave=wave, mode=mode).to(dev)
iwt = DWTInverse(wave=wave, mode=mode).to(dev)
yl, yh = dwt(x)
x2 = iwt((yl, yh))
# Can have data errors sometimes
assert yl.is_contiguous()
for j in range(J):
assert yh[j].is_contiguous()
assert x2.is_contiguous()
@pytest.mark.parametrize("wave, J, mode", [
('db1', 1, 'zero'),
('db1', 3, 'zero'),
('db3', 1, 'symmetric'),
('db3', 2, 'reflect'),
('db2', 3, 'periodization'),
('db2', 3, 'periodic'),
('db4', 2, 'zero'),
('db3', 3, 'symmetric'),
('bior2.4', 2, 'periodization'),
('bior2.4', 2, 'periodization')])
def test_equal(wave, J, mode):
x = torch.randn(5, 4, 64, 64).to(dev)
dwt = DWTForward(J=J, wave=wave, mode=mode).to(dev)
iwt = DWTInverse(wave=wave, mode=mode).to(dev)
yl, yh = dwt(x)
x2 = iwt((yl, yh))
# Test the forward and inverse worked
np.testing.assert_array_almost_equal(x.cpu(), x2.detach().cpu(), decimal=PREC_FLT)
# Test it is the same as doing the PyWavelets wavedec with reflection
# padding
coeffs = pywt.wavedec2(x.cpu().numpy(), wave, level=J, axes=(-2,-1),
mode=mode)
np.testing.assert_array_almost_equal(yl.cpu(), coeffs[0], decimal=PREC_FLT)
for j in range(J):
for b in range(3):
np.testing.assert_array_almost_equal(
coeffs[J-j][b], yh[j][:,:,b].cpu(), decimal=PREC_FLT)
@pytest.mark.parametrize("size", [
(64, 64), (127, 127), (126, 127), (100, 99), (99, 100)])
def test_equal_oddshape(size):
wave = 'db3'
J = 3
mode = 'symmetric'
x = torch.randn(5, 4, *size).to(dev)
dwt1 = DWTForward(J=J, wave=wave, mode=mode).to(dev)
iwt1 = DWTInverse(wave=wave, mode=mode).to(dev)
yl1, yh1 = dwt1(x)
x1 = iwt1((yl1, yh1))
# Test it is the same as doing the PyWavelets wavedec
coeffs = pywt.wavedec2(x.cpu().numpy(), wave, level=J, axes=(-2,-1), mode=mode)
X = pywt.waverec2(coeffs, wave, mode=mode)
np.testing.assert_array_almost_equal(X, x1.detach().cpu(), decimal=PREC_FLT)
np.testing.assert_array_almost_equal(yl1.cpu(), coeffs[0], decimal=PREC_FLT)
for j in range(J):
for b in range(3):
np.testing.assert_array_almost_equal(
coeffs[J-j][b], yh1[j][:,:,b].cpu(), decimal=PREC_FLT)
@pytest.mark.parametrize("size", [
(64, 64), (127, 127), (126, 127), (100, 99), (99, 100)])
def test_equal_oddshape2(size):
wave = 'db3'
J = 3
mode = 'periodization'
x = torch.randn(5, 4, *size).to(dev)
dwt1 = DWTForward(J=J, wave=wave, mode=mode).to(dev)
iwt1 = DWTInverse(wave=wave, mode=mode).to(dev)
yl1, yh1 = dwt1(x)
x1 = iwt1((yl1, yh1))
# Test it is the same as doing the PyWavelets wavedec
coeffs = pywt.wavedec2(x.cpu().numpy(), wave, level=J, axes=(-2,-1), mode=mode)
X = pywt.waverec2(coeffs, wave, mode=mode)
np.testing.assert_array_almost_equal(X, x1.detach().cpu(), decimal=PREC_FLT)
np.testing.assert_array_almost_equal(yl1.cpu(), coeffs[0], decimal=PREC_FLT)
for j in range(J):
for b in range(3):
np.testing.assert_array_almost_equal(
coeffs[J-j][b], yh1[j][:,:,b].cpu(), decimal=PREC_FLT)
@pytest.mark.parametrize("wave, J, mode", [
('db1', 1, 'zero'),
('db1', 3, 'zero'),
('db3', 1, 'symmetric'),
('db3', 2, 'reflect'),
('db2', 3, 'periodization'),
('db2', 3, 'periodic'),
('db4', 2, 'zero'),
('db3', 3, 'symmetric'),
('bior2.4', 2, 'periodization'),
('bior2.4', 2, 'periodization')])
def test_equal_double(wave, J, mode):
with set_double_precision():
x = torch.randn(5, 4, 64, 64).to(dev)
assert x.dtype == torch.float64
dwt = DWTForward(J=J, wave=wave, mode=mode).to(dev)
iwt = DWTInverse(wave=wave, mode=mode).to(dev)
yl, yh = dwt(x)
x2 = iwt((yl, yh))
# Test the forward and inverse worked
np.testing.assert_array_almost_equal(x.cpu(), x2.detach().cpu(), decimal=PREC_DBL)
coeffs = pywt.wavedec2(x.cpu().numpy(), wave, level=J, axes=(-2,-1), mode=mode)
np.testing.assert_array_almost_equal(yl.cpu(), coeffs[0], decimal=7)
for j in range(J):
for b in range(3):
np.testing.assert_array_almost_equal(
coeffs[J-j][b], yh[j][:,:,b].cpu(), decimal=PREC_DBL)
@pytest.mark.parametrize("wave, J, j", [
('db1', 1, 0),
('db1', 2, 1),
('db2', 2, 0),
('db3', 3, 2)
])
def test_commutativity(wave, J, j):
# Test the commutativity of the dwt
C = 3
Y = torch.randn(4, C, 128, 128, requires_grad=True, device=dev)
dwt = DWTForward(J=J, wave=wave).to(dev)
iwt = DWTInverse(wave=wave).to(dev)
coeffs = dwt(Y)
coeffs_zero = dwt(torch.zeros_like(Y))
# Set level j LH to be nonzero
coeffs_zero[1][j][:,:,0] = coeffs[1][j][:,:,0]
ya = iwt(coeffs_zero)
# Set level j HL to also be nonzero
coeffs_zero[1][j][:,:,1] = coeffs[1][j][:,:,1]
yab = iwt(coeffs_zero)
# Set level j LH to be nonzero
coeffs_zero[1][j][:,:,0] = torch.zeros_like(coeffs[1][j][:,:,0])
yb = iwt(coeffs_zero)
# Set level j HH to also be nonzero
coeffs_zero[1][j][:,:,2] = coeffs[1][j][:,:,2]
ybc = iwt(coeffs_zero)
# Set level j HL to be nonzero
coeffs_zero[1][j][:,:,1] = torch.zeros_like(coeffs[1][j][:,:,1])
yc = iwt(coeffs_zero)
np.testing.assert_array_almost_equal(
(ya+yb).detach().cpu(), yab.detach().cpu(), decimal=PREC_FLT)
np.testing.assert_array_almost_equal(
(yc+yb).detach().cpu(), ybc.detach().cpu(), decimal=PREC_FLT)
# Test gradients
@pytest.mark.parametrize("wave, J, mode", [
('db1', 1, 'zero'),
('db1', 3, 'zero'),
('db3', 1, 'symmetric'),
('db3', 2, 'reflect'),
('db2', 3, 'periodization'),
('db4', 2, 'zero'),
('bior2.4', 2, 'periodization'),
('db1', 1, 'zero'),
('db1', 3, 'zero'),
('db2', 3, 'periodization'),
('db4', 2, 'zero'),
('bior2.4', 2, 'periodization')
])
def test_gradients_fwd(wave, J, mode):
""" Gradient of forward function should be inverse function with filters
swapped """
im = np.random.randn(5,6,128, 128).astype('float32')
imt = torch.tensor(im, dtype=torch.float32, requires_grad=True, device=dev)
wave = pywt.Wavelet(wave)
fwd_filts = (wave.dec_lo, wave.dec_hi)
inv_filts = (wave.dec_lo[::-1], wave.dec_hi[::-1])
dwt = DWTForward(J=J, wave=fwd_filts, mode=mode).to(dev)
iwt = DWTInverse(wave=inv_filts, mode=mode).to(dev)
yl, yh = dwt(imt)
# Test the lowpass
ylg = torch.randn(*yl.shape, device=dev)
yl.backward(ylg, retain_graph=True)
zeros = [torch.zeros_like(yh[i]) for i in range(J)]
ref = iwt((ylg, zeros))
np.testing.assert_array_almost_equal(imt.grad.detach().cpu(), ref.cpu(),
decimal=PREC_FLT)
# Test the bandpass
for j, y in enumerate(yh):
imt.grad.zero_()
g = torch.randn(*y.shape, device=dev)
y.backward(g, retain_graph=True)
hps = [zeros[i] for i in range(J)]
hps[j] = g
ref = iwt((torch.zeros_like(yl), hps))
np.testing.assert_array_almost_equal(imt.grad.detach().cpu(), ref.cpu(),
decimal=PREC_FLT)
# Test gradients
@pytest.mark.parametrize("wave, J, mode", [
('db1', 1, 'zero'),
('db1', 3, 'zero'),
('db3', 1, 'symmetric'),
('db3', 2, 'reflect'),
('db2', 3, 'periodization'),
('db4', 2, 'zero'),
# ('db3', 3, 'symmetric', False, False),
('bior2.4', 2, 'periodization'),
('db1', 1, 'zero'),
('db1', 3, 'zero'),
# ('db3', 1, 'symmetric', True, True),
# ('db3', 2, 'reflect', False, True),
('db2', 3, 'periodization'),
('db4', 2, 'zero'),
# ('db3', 3, 'symmetric', True, False),
('bior2.4', 2, 'periodization')
])
def test_gradients_inv(wave, J, mode):
""" Gradient of inverse function should be forward function with filters
swapped """
wave = pywt.Wavelet(wave)
fwd_filts = (wave.dec_lo, wave.dec_hi)
inv_filts = (wave.dec_lo[::-1], wave.dec_hi[::-1])
dwt = DWTForward(J=J, wave=fwd_filts, mode=mode).to(dev)
iwt = DWTInverse(wave=inv_filts, mode=mode).to(dev)
# Get the shape of the pyramid
temp = torch.zeros(5,6,128,128).to(dev)
l, h = dwt(temp)
# Create our inputs
yl = torch.randn(*l.shape, requires_grad=True, device=dev)
yh = [torch.randn(*h[i].shape, requires_grad=True, device=dev)
for i in range(J)]
y = iwt((yl, yh))
# Test the gradients
yg = torch.randn(*y.shape, device=dev)
y.backward(yg, retain_graph=True)
dyl, dyh = dwt(yg)
# test the lowpass
np.testing.assert_array_almost_equal(yl.grad.detach().cpu(), dyl.cpu(),
decimal=PREC_FLT)
# Test the bandpass
for j in range(J):
np.testing.assert_array_almost_equal(yh[j].grad.detach().cpu(),
dyh[j].cpu(),
decimal=PREC_FLT)
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