nn¶
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class
torchvectorized.nn.EigVals¶ Differentiable neural network layer (
torch.nn.Module) that performs matrix exponential on every voxel in a volume of flattened 3x3 symmetric matrices of shape Bx9xDxHxW.See Ionescu et al., Matrix backpropagation for deep networks with structured layers, CVPR 2015 for details on the gradients computation
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forward(x: torch.Tensor)¶ Takes a volume of flattened 3x3 symmetric matrices of shape Bx9xDxHxW and return a volume of their eigenvalues
Parameters: x (torch.Tensor) – A volume of flattened 3x3 symmetric matrices of shape Bx9xDxHxW Returns: A tensor with shape (B*D*H*W)x3 where every voxel’s channels are the eigenvalues of the inpur matrix at the same spatial location. Return type: torch.Tensor
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class
torchvectorized.nn.Expm¶ Differentiable neural network layer (
torch.nn.Module) that performs matrix exponential on every voxel in a volume of flattened 3x3 symmetric matrices of shape Bx9xDxHxW.See Ionescu et al., Matrix backpropagation for deep networks with structured layers, CVPR 2015 for details on the gradients computation
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forward(x: torch.Tensor)¶ Compute the matrix exponential
of
every voxel in a volume of flattened 3x3 symmetric matrices of shape Bx9xDxHxW.Parameters: x (torch.Tensor) – A volume of flattened 3x3 symmetric matrices of shape Bx9xDxHxW Returns: A volume of flattened 3x3 symmetric matrices of shape Bx9xDxHxW. Return type: torch.Tensor
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class
torchvectorized.nn.Logm¶ Differentiable neural network layer (
torch.nn.Module) that performs matrix logarithm on every voxel in a volume of flattened 3x3 symmetric matrices of shape Bx9xDxHxW.See Ionescu et al., Matrix backpropagation for deep networks with structured layers, CVPR 2015 for details on the gradients computation
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forward(x: torch.Tensor)¶ Compute the matrix exponential
of
every voxel in a volume of flattened 3x3 symmetric matrices of shape Bx9xDxHxW.Parameters: x (torch.Tensor) – A volume of flattened 3x3 symmetric matrices of shape Bx9xDxHxW Returns: A volume of flattened 3x3 symmetric matrices of shape Bx9xDxHxW. Return type: torch.Tensor
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class
torchvectorized.nn.ExpmLogm¶ Differentiable neural network layer (
torch.nn.Module) that performs consecutive matrix exponential and logarithm on every voxel in a volume of flattened 3x3 symmetric matrices of shape Bx9xDxHxW.See Ionescu et al., Matrix backpropagation for deep networks with structured layers, CVPR 2015 for details on the gradients computation
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forward(x: torch.Tensor)¶ Compute the matrix exponential
and
the matrix logarithm of every voxel
in a volume of flattened 3x3 symmetric matrices of shape Bx9xDxHxW.Parameters: x (torch.Tensor) – A volume of flattened 3x3 symmetric matrices of shape Bx9xDxHxW Returns: A volume of flattened 3x3 symmetric matrices of shape Bx9xDxHxW. Return type: torch.Tensor
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