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)
def upsample2x(tensor, interpolation='linear'):
if struct.isstruct(tensor):
return struct.map(lambda s: upsample2x(s, interpolation), tensor, recursive=False)
if interpolation.lower() != 'linear':
raise ValueError('Only linear interpolation supported')
dims = range(spatial_rank(tensor))
vlen = tensor.shape[-1]
spatial_dims = tensor.shape[1:-1]
rank = spatial_rank(tensor)
tensor = math.pad(tensor, _get_pad_width(rank), 'replicate')
for dim in dims:
lower, center, upper = _dim_shifted(tensor, dim, (-1, 0, 1))
combined = math.stack([0.25 * lower + 0.75 * center, 0.75 * center + 0.25 * upper], axis=2 + dim)
tensor = math.reshape(combined, [-1] + [spatial_dims[dim] * 2 if i == dim else tensor.shape[i + 1] for i in dims] + [vlen])
return tensor