src.model.deeplearn.layer.strided_kpconv_layer

Classes

StridedKPConvLayer(*args, **kwargs)

class src.model.deeplearn.layer.strided_kpconv_layer.StridedKPConvLayer(*args, **kwargs)

Author:

Alberto M. Esmoris Pena

Strided version of the KPConvLayer layer. Instead of transforming \(R\) input points with \(D_{\mathrm{in}}\) features into \(R\) output points with \(D_{\mathrm{out}}\) features, it transforms \(R_1\) input points with \(D_{\mathrm{in}}\) feautres into \(R_2\) output points with \(D_{\mathrm{out}}\) features, where typically \(R_1 > R_2\).

See KPConvLayer.

__init__(**kwargs)

See Layer and Layer.__init__(). Also, see KPConvLayer and KPConvLayer.__init__().

build(dim_in)

See KPConvLayer and KPConvLayer.build().

call(inputs, training=False, mask=False)

Compute the strided version of KPConv. The main difference with respect to the KPConvLayer layer is that the \(\kappa\) neighbor points \(\pmb{x}_{j*} \in \mathcal{N}^{D}_{\pmb{x}_{i*}}\) are now defined such that \(\pmb{x}_{i*}\) is a point from a downsampled structure space \(\pmb{X_b} \in \mathbb{R}^{R_2 \times n_x}\), while the \(\pmb{x}_{j*}\) points belong to the structure space before downsampling \(\pmb{X_a} \in \mathbb{R}^{R_1 \times n_x}\).

Parameters:

inputs –

The input such that:

– inputs[0]

is the structure space tensor representing the geometry of the many receptive fields in the batch.

\[\mathcal{X_a} \in \mathbb{R}^{K \times R_1 \times n_x}\]

– inputs[1]

is the structure space tensor representing the geometry of the many downsampled receptive fields in the batch.

\[\mathcal{X_b} \in \mathbb{R}^{K \times R_2 \times n_x}\]

– inputs[2]

is the feature space tensor representing the features of the many receptive fields in the batch.

\[\mathcal{F} \in \mathbb{R}^{K \times R_1 \times n_f}\]

– inputs[3]

is the indexing tensor representing the neighborhoods of \(\kappa\) neighbors in the non downsampled space for each point in the downsampled.

\[\mathcal{N}^D \in \mathbb{Z}^{K \times R_2 \times \kappa}\]

Returns:

The output feature space \(\mathcal{Y} \in \mathbb{R}^{K \times R_2 \times D_{\mathrm{out}}}\).