import torch
[docs]class SparseMultiConv2D(torch.nn.Module):
"""
Sparse implementation of a Multi 2D Convolutional 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 padding to use
"""
factory_kwargs = {'device': device, 'dtype': dtype}
super().__init__()
for i in range(kernel.shape[0]):
self.register_buffer(
f'w_{i}',
torch.Tensor(kernel[i]).to(**factory_kwargs).to_sparse()
)
self.register_buffer(
f'b_{i}',
torch.Tensor(bias[i])
)
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
"""
outputs = []
for i in range(len(inputs)):
outputs.append(
torch.nn.functional.conv2d(
inputs[i],
self.get_buffer(f'w_{i}').to_dense(),
self.get_buffer(f'b_{i}').to_dense(),
stride=self.strides,
padding=self.padding
)
)
return outputs