def _conv_laplace_3d(tensor):
"""
3D/Cube laplace stencil in 3D+2D [3,3,3,1,1]
array([[[[[ 0.]], [[ 0.]], [[ 0.]]],
[[[ 0.]], [[ 1.]], [[ 0.]]],
[[[ 0.]], [[ 0.]], [[ 0.]]]],
[[[[ 0.]], [[ 1.]], [[ 0.]]],
[[[ 1.]], [[-6.]], [[ 1.]]],
[[[ 0.]], [[ 1.]], [[ 0.]]]],
[[[[ 0.]], [[ 0.]], [[ 0.]]],
[[[ 0.]], [[ 1.]], [[ 0.]]],
[[[ 0.]], [[ 0.]], [[ 0.]]]]]
returns ...
padding explicitly done in laplace(), hence here not needed
"""
kernel = math.to_float([[[0., 0., 0.], [0., 1., 0.], [0., 0., 0.]],
[[0., 1., 0.], [1., -6., 1.], [0., 1., 0.]],
[[0., 0., 0.], [0., 1., 0.], [0., 0., 0.]]])
kernel = kernel.reshape((3, 3, 3, 1, 1))
if tensor.shape[-1] == 1:
return math.conv(tensor, kernel, padding='VALID')
else:
return math.concat([
math.conv(tensor[..., i:i + 1], kernel, padding='VALID')
for i in range(tensor.shape[-1])
], -1)
def _conv_laplace_2d(tensor):
kernel = np.array([[0., 1., 0.], [1., -4., 1.], [0., 1., 0.]], dtype=np.float32)
kernel = kernel.reshape((3, 3, 1, 1))
if tensor.shape[-1] == 1:
return math.conv(tensor, kernel, padding='VALID')
else:
return math.concat([math.conv(tensor[..., i:i + 1], kernel, padding='VALID') for i in range(tensor.shape[-1])], -1)
def blur(field, radius, cutoff=None, kernel="1/1+x"):
"""
Warning: This function can cause NaN in the gradients, reason unknown.
Runs a blur kernel over the given tensor.
:param field: tensor
:param radius: weight function curve scale
:param cutoff: kernel size
:param kernel: Type of blur kernel (str). Must be in ('1/1+x', 'gauss')
:return:
"""
if cutoff is None:
cutoff = min(int(round(radius * 3)), *field.shape[1:-1])
xyz = np.meshgrid(
*[range(-int(cutoff), (cutoff) + 1) for _ in field.shape[1:-1]])
d = math.to_float(np.sqrt(np.sum([x**2 for x in xyz], axis=0)))
if kernel == "1/1+x":
weights = math.to_float(1) / (d / radius + 1)
elif kernel.lower() == "gauss":
weights = math.exp(-d / radius / 2)
else:
raise ValueError("Unknown kernel: %s" % kernel)
weights /= math.sum(weights)
weights = math.reshape(weights, list(weights.shape) + [1, 1])
return math.conv(field, weights)