tfg.geometry.convolution.graph_convolution.feature_steered_convolution

Implements the Feature Steered graph convolution.

tfg.geometry.convolution.graph_convolution.feature_steered_convolution(
    data: type_alias.TensorLike,
    neighbors: tf.sparse.SparseTensor,
    sizes: type_alias.TensorLike,
    var_u: type_alias.TensorLike,
    var_v: type_alias.TensorLike,
    var_c: type_alias.TensorLike,
    var_w: type_alias.TensorLike,
    var_b: type_alias.TensorLike,
    name=&#x27;graph_convolution_feature_steered_convolution'
) -> tf.Tensor

FeaStNet: Feature-Steered Graph Convolutions for 3D Shape Analysis Nitika Verma, Edmond Boyer, Jakob Verbeek CVPR 2018 https://arxiv.org/abs/1706.05206

The shorthands used below are V: The number of vertices. C: The number of channels in the input data. D: The number of channels in the output after convolution. W: The number of weight matrices used in the convolution. The input variables (var_u, var_v, var_c, var_w, var_b) correspond to the variables with the same names in the paper cited above.

Note
In the following, A1 to An are optional batch dimensions.

Args
`data`	A `float` tensor with shape `[A1, ..., An, V, C]`.
`neighbors`	A `SparseTensor` with the same type as `data` and with shape `[A1, ..., An, V, V]` representing vertex neighborhoods. The neighborhood of a vertex defines the support region for convolution. For a mesh, a common choice for the neighborhood of vertex i would be the vertices in the K-ring of i (including i itself). Each vertex must have at least one neighbor. For a faithful implementation of the FeaStNet convolution, neighbors should be a row-normalized weight matrix corresponding to the graph adjacency matrix with self-edges: `neighbors[A1, ..., An, i, j] > 0` if vertex j is a neighbor of i, and `neighbors[A1, ..., An, i, i] > 0` for all i, and `sum(neighbors, axis=-1)[A1, ..., An, i] == 1.0 for all i`. These requirements are relaxed in this implementation.
`sizes`	An `int` tensor of shape `[A1, ..., An]` indicating the true input sizes in case of padding (`sizes=None` indicates no padding).Note that `sizes[A1, ..., An] <= V`. If `data` and `neighbors` are 2-D, `sizes` will be ignored. An example usage of `sizes`: consider an input consisting of three graphs G0, G1, and G2 with V0, V1, and V2 vertices respectively. The padded input would have the following shapes: `data.shape = [3, V, C]` and `neighbors.shape = [3, V, V]`, where `V = max([V0, V1, V2])`. The true sizes of each graph will be specified by `sizes=[V0, V1, V2]`, `data[i, :Vi, :]` and `neighbors[i, :Vi, :Vi]` will be the vertex and neighborhood data of graph Gi. The `SparseTensor` `neighbors` should have no nonzero entries in the padded regions.
`var_u`	A 2-D tensor with shape `[C, W]`.
`var_v`	A 2-D tensor with shape `[C, W]`.
`var_c`	A 1-D tensor with shape `[W]`.
`var_w`	A 3-D tensor with shape `[W, C, D]`.
`var_b`	A 1-D tensor with shape `[D]`.
`name`	A name for this op. Defaults to `graph_convolution_feature_steered_convolution`.

Returns
Tensor with shape `[A1, ..., An, V, D]`.

Raises
`TypeError`	if the input types are invalid.
`ValueError`	if the input dimensions are invalid.

tfg.geometry.convolution.graph_convolution.feature_steered_convolution

Note

Args

Returns

Raises