import torch
[docs]class SparseConv3D(torch.nn.Module):
"""
Sparse 3D Convolutional layer, expected to be converted from a
trained, pruned layer
"""
def __init__(
self,
kernel,
bias,
padding='same',
strides=1,
device=None,
dtype=None
):
"""
Parameters
----------
kernel : torch.Tensor or Tensor-like
The kernel to use
bias : torch.Tensor or Tensor-like
The bias to use
padding : str or int (default 'same')
The padding to use
strides : int or tuple (default 1)
The strides to use
"""
factory_kwargs = {'device': device, 'dtype': dtype}
super().__init__()
self.register_buffer('w', torch.Tensor(
kernel).to(**factory_kwargs).to_sparse())
self.register_buffer('b', torch.Tensor(
bias).to(**factory_kwargs).to_sparse())
self.padding = padding
self.strides = strides
[docs] def forward(
self,
inputs
):
"""
Call the layer on input data
Parameters
----------
inputs : torch.Tensor
Inputs to call the layer's logic on
Returns
-------
results : torch.Tensor
The results of the layer's logic
"""
kernel = self.w.to_dense()
bias = self.b.to_dense()
return torch.nn.functional.conv3d(
inputs,
kernel,
bias,
stride=self.strides,
padding=self.padding
)