nn¶
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class
torchvectorized.nn.
EigVals
¶ Differentiable neural network layer (
torch.nn.Module
) that performs eigendecomposition on every voxel in a volume of flattened 3x3 symmetric matrices of shape Bx9xDxHxW and return the eigenvalues.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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