networks
Networks for AID
WideNet
- class indexedconv.nets.aid.WideNet(n_out)
ResNet like Network from HexaConv paper (Z²).
- Parameters:
n_out (int) – Number of features after last convolution.
WideNetIndexConvIndexPool
- class indexedconv.nets.aid.WideNetIndexConvIndexPool(index_matrix, camera_layout, n_out)
ResNet like Network from HexaConv paper implemented with indexed convolutions and pooling.
- Parameters:
index_matrix (torch.Tensor) – The index matrix corresponding to the input images.
camera_layout (str) – The grid shape of the images.
n_out (int) – Number of features after last convolution.
WideNetMasked
- class indexedconv.nets.aid.WideNetMasked(n_out)
ResNet like Network from HexaConv paper implementing masked convolutions.
- Parameters:
n_out (int) – Number of features after last convolution.
Networks for CIFAR
WideNet
- class indexedconv.nets.cifar.WideNet
ResNet like Network from HexaConv paper (Z²).
WideNetIndexConvIndexPool
- class indexedconv.nets.cifar.WideNetIndexConvIndexPool(index_matrix, camera_layout)
ResNet like Network from HexaConv paper implemented with indexed convolutions and pooling.
- Parameters:
index_matrix (torch.Tensor) – The index matrix corresponding to the input images.
camera_layout (str) – The grid shape of the images.
WideNetIndexConvIndexPoolRetina
- class indexedconv.nets.cifar.WideNetIndexConvIndexPoolRetina(index_matrix, camera_layout)
ResNet like Network from HexaConv paper implemented with indexed convolutions (retina like kernel) and pooling.
- Parameters:
index_matrix (torch.Tensor) – The index matrix corresponding to the input images.
camera_layout (str) – The grid shape of the images.
Network for MNIST
GLNet2HexaConvForMnist
- class indexedconv.nets.mnist.GLNet2HexaConvForMnist(index_matrix)
Network with indexed convolutions and pooling (square kernels). 2 CL (after each conv layer, pooling is executed) 1 FC
- Parameters:
index_matrix (torch.Tensor) – The index matrix corresponding to the input images.