|View source on GitHub|
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 = 'graph_pooling_unpool' ) -> tf.Tensor
Upsamples a graph by applying a pooling map in reverse. The inputs
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.
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
||A name for this op. Defaults to 'graph_pooling_unpool'.|
Tensor with shape
||if the input types are invalid.|
||if the input dimensions are invalid.|