src.model.deeplearn.layer.strided_light_kpconv_layer
Classes
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- class src.model.deeplearn.layer.strided_light_kpconv_layer.StridedLightKPConvLayer(*args, **kwargs)
- Author:
Alberto M. Esmoris Pena
Strided version of the
LightKPConvLayerlayer. 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 \(D_{\mathrm{in}}\) features into \(R_2\) output points with \(D_{\mathrm{out}}\) features, where typically \(R_1 > R_2\).See
LightKPConvLayerandStridedKPConvLayer.- __init__(**kwargs)
See
LayerandLayer.__init__(). Also, seeLightKPConvLayerandLightKPConvLayer.__init__().
- build(dim_in)
See
LightKPConvLayerandLightKPConvLayer.build().
- call(inputs, training=False, mask=False)
Compute the strided version of light KPConv. The main difference with respect to the
LightKPConvLayerlayer 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 \(\pmb{X_a} \in \mathbb{R}^{R_1 \times n_x}\).See
LightKPConvLayer,StridedKPConvLayer, andStridedKPConvLayer.call().- 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 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 \(\mathcal{Y} \in \mathbb{R}^{K \times R_2 \times D_{\mathrm{out}}}\).