def testRescaleAndUnrescaleReproducesInput(self):
"""Tests that rescale(rescale(x, k), 1/k) = x."""
im = np.random.uniform(size=(2, 32, 32))
scale_base = np.exp(np.random.normal())
pyr = wavelet.construct(im, 4, 'LeGall5/3')
pyr_rescaled = wavelet.rescale(pyr, scale_base)
pyr_recon = wavelet.rescale(pyr_rescaled, 1. / scale_base)
self._assert_pyramids_close(pyr, pyr_recon, 1e-8)
def transform_to_mat(self, x):
"""Transforms a batch of images to a matrix."""
assert len(x.shape) == 4
x = torch.as_tensor(x)
if self.color_space == 'YUV':
x = util.rgb_to_syuv(x)
# If `color_space` == 'RGB', do nothing.
# Reshape `x` from
# (num_batches, width, height, num_channels) to
# (num_batches * num_channels, width, height)
_, width, height, num_channels = x.shape
x_stack = torch.reshape(x.permute(0, 3, 1, 2), (-1, width, height))
# Turn each channel in `x_stack` into the spatial representation specified
# by `representation`.
if self.representation in wavelet.generate_filters():
x_stack = wavelet.flatten(
wavelet.rescale(
wavelet.construct(x_stack, self.wavelet_num_levels,
self.representation),
self.wavelet_scale_base))
elif self.representation == 'DCT':
x_stack = util.image_dct(x_stack)
# If `representation` == 'PIXEL', do nothing.
# Reshape `x_stack` from
# (num_batches * num_channels, width, height) to
# (num_batches, num_channels * width * height)
x_mat = torch.reshape(
torch.reshape(x_stack, (-1, num_channels, width, height)).permute(
0, 2, 3, 1), [-1, width * height * num_channels])
return x_mat
def testRescaleOneHalfIsNormalized(self):
"""Tests that rescale(construct(k), 0.5)[-1] = k for constant image k."""
for num_levels in range(5):
k = np.random.uniform()
im = k * np.ones((2, 32, 32))
pyr = wavelet.construct(im, num_levels, 'LeGall5/3')
pyr_rescaled = wavelet.rescale(pyr, 0.5)
np.testing.assert_allclose(pyr_rescaled[-1],
k * np.ones_like(pyr_rescaled[-1]),
atol=1e-8,
rtol=1e-8)
def testRescaleDoesNotAffectTheFirstLevel(self):
"""Tests that rescale(x, s)[0] = x[0] for any s."""
im = np.random.uniform(size=(2, 32, 32))
pyr = wavelet.construct(im, 4, 'LeGall5/3')
pyr_rescaled = wavelet.rescale(pyr, np.exp(np.random.normal()))
self._assert_pyramids_close(pyr[0:1], pyr_rescaled[0:1], 1e-8)
def testRescaleOneIsANoOp(self):
"""Tests that rescale(x, 1) = x."""
im = np.random.uniform(size=(2, 32, 32))
pyr = wavelet.construct(im, 4, 'LeGall5/3')
pyr_rescaled = wavelet.rescale(pyr, 1.)
self._assert_pyramids_close(pyr, pyr_rescaled, 1e-8)
