tfg.geometry.convolution.graph_pooling.unpool

Graph upsampling by inverting the pooling map.

tfg.geometry.convolution.graph_pooling.unpool(
    data: type_alias.TensorLike,
    pool_map: type_alias.TensorLike,
    sizes: type_alias.TensorLike,
    name: str = &#x27;graph_pooling_unpool'
) -> tf.Tensor

Upsamples a graph by applying a pooling map in reverse. The inputs pool_map and sizes are the same as used for pooling:

pooled = pool(data, pool_map, sizes)
upsampled = unpool(pooled, pool_map, sizes)

The shorthands used below are V1: The number of vertices in the input data. V2: The number of vertices in the unpooled output data. C: The number of channels in the data.

Note
In the following, A1 to A3 are optional batch dimensions. Only up to three batch dimensions are supported due to limitations with TensorFlow's dense-sparse multiplication.

Please see the documentation for graph_pooling.pool for a detailed interpretation of the inputs pool_map and sizes.

Args
`data`	A `float` tensor with shape `[A1, ..., A3, V1, C]`.
`pool_map`	A `SparseTensor` with the same type as `data` and with shape `[A1, ..., A3, V1, V2]`. The features for vertex `v1` are computed by pooling over the entries in `pool_map[A1, ..., A3, v1, :]`. This function applies this pooling map in reverse.
`sizes`	An `int` tensor of shape `[A1, ..., A3, 2]` indicating the true input sizes in case of padding (`sizes=None` indicates no padding): `sizes[A1, ..., A3, 0] <= V1` and `sizes[A1, ..., A3, 1] <= V2`.
`name`	A name for this op. Defaults to 'graph_pooling_unpool'.

Returns
Tensor with shape `[A1, ..., A3, V2, C]`.

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

tfg.geometry.convolution.graph_pooling.unpool

Note

Args

Returns

Raises