def __init__(self, permutation, dim=1): if permutation.ndimension() != 1: raise ValueError("Permutation must be a 1D tensor.") if not check.is_positive_int(dim): raise ValueError("dim must be a positive integer.") super().__init__() self._dim = dim self.register_buffer("_permutation", permutation)
def __init__(self, features, num_transforms): """Constructor. Args: features: int, dimensionality of the input. num_transforms: int, number of Householder transforms to use. Raises: TypeError: if arguments are not the right type. """ if not check.is_positive_int(features): raise TypeError("Number of features must be a positive integer.") if not check.is_positive_int(num_transforms): raise TypeError("Number of transforms must be a positive integer.") super().__init__() self.features = features self.num_transforms = num_transforms # TODO: are randn good initial values? # these vectors are orthogonal to the hyperplanes through which we reflect # self.q_vectors = nets.Parameter(torch.randn(num_transforms, features)) # self.q_vectors = nets.Parameter(torch.eye(num_transforms // 2, features)) import numpy as np def tile(a, dim, n_tile): if a.nelement() == 0: return a init_dim = a.size(dim) repeat_idx = [1] * a.dim() repeat_idx[dim] = n_tile a = a.repeat(*(repeat_idx)) order_index = torch.Tensor( np.concatenate( [init_dim * np.arange(n_tile) + i for i in range(init_dim)] ) ).long() return torch.index_select(a, dim, order_index) qv = tile(torch.eye(num_transforms // 2, features), 0, 2) if np.mod(num_transforms, 2) != 0: # odd number of transforms, including 1 qv = torch.cat((qv, torch.zeros(1, features))) qv[-1, num_transforms // 2] = 1 self.q_vectors = nn.Parameter(qv)
def merge_leading_dims(x, num_dims): """Reshapes the tensor `x` such that the first `num_dims` dimensions are merged to one.""" if not check.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 check.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, eps=1e-5, momentum=0.1, affine=True): if not check.is_positive_int(features): raise TypeError("Number of features must be a positive integer.") super().__init__() self.momentum = momentum self.eps = eps constant = np.log(np.exp(1 - eps) - 1) self.unconstrained_weight = nn.Parameter(constant * torch.ones(features)) self.bias = nn.Parameter(torch.zeros(features)) self.register_buffer("running_mean", torch.zeros(features)) self.register_buffer("running_var", torch.zeros(features))
def __init__(self, features): """ Transform that performs activation normalization. Works for 2D and 4D inputs. For 4D inputs (images) normalization is performed per-channel, assuming BxCxHxW input shape. Reference: > D. Kingma et. al., Glow: Generative flow with invertible 1x1 convolutions, NeurIPS 2018. """ if not check.is_positive_int(features): raise TypeError("Number of features must be a positive integer.") super().__init__() self.register_buffer("initialized", torch.tensor(False, dtype=torch.bool)) self.log_scale = nn.Parameter(torch.zeros(features)) self.shift = nn.Parameter(torch.zeros(features))
def __init__(self, num_transforms, split_dim=1): """Constructor. Args: num_transforms: int, total number of transforms to be added. split_dim: dimension along which to split. """ if not check.is_positive_int(split_dim): raise TypeError("Split dimension must be a positive integer.") super().__init__() self._transforms = nn.ModuleList() self._output_shapes = [] self._num_transforms = num_transforms self._split_dim = split_dim
def __init__(self, features, dim=1): if not check.is_positive_int(features): raise ValueError("Number of features must be a positive integer.") super().__init__(torch.arange(features - 1, -1, -1), dim)