def repeat_rows(x, num_reps):
"""Each row of tensor `x` is repeated `num_reps` times along leading dimension."""
if not utils2.is_positive_int(num_reps):
raise TypeError('Number of repetitions must be a positive integer.')
shape = x.shape
x = x.unsqueeze(1)
x = x.expand(shape[0], num_reps, *shape[1:])
return merge_leading_dims(x, num_dims=2)
def __init__(self, permutation, dim=1):
if permutation.ndimension() != 1:
raise ValueError('Permutation must be a 1D tensor.')
if not utils.is_positive_int(dim):
raise ValueError('dim must be a positive integer.')
super().__init__()
self._dim = dim
self.register_buffer('_permutation', permutation)
def tile(x, n):
if not utils2.is_positive_int(n):
raise TypeError('Argument \'n\' must be a positive integer.')
x_ = x.reshape(-1)
x_ = x_.repeat(n)
x_ = x_.reshape(n, -1)
x_ = x_.transpose(1, 0)
x_ = x_.reshape(-1)
return x_
def merge_leading_dims(x, num_dims):
"""Reshapes the tensor `x` such that the first `num_dims` dimensions are merged to one."""
if not utils2.is_positive_int(num_dims):
raise TypeError('Number of leading dims must be a positive integer.')
if num_dims > x.dim():
raise ValueError(
'Number of leading dims can\'t be greater than total number of dims.'
)
new_shape = torch.Size([-1]) + x.shape[num_dims:]
return torch.reshape(x, new_shape)
def __init__(self, features, using_cache=False):
if not utils.is_positive_int(features):
raise TypeError('Number of features must be a positive integer.')
super().__init__()
self.features = features
self.bias = nn.Parameter(torch.zeros(features))
# Caching flag and values.
self.using_cache = using_cache
self.cache = LinearCache()
def __init__(self, features, dim=1):
if not utils.is_positive_int(features):
raise ValueError('Number of features must be a positive integer.')
super().__init__(torch.arange(features - 1, -1, -1), dim)