cnn_convolutions¶
Convolutional building blocks for use in CNN models.
- class graphnet.models.components.cnn_convolutions.Conv3dBN(in_channels, out_channels, kernel_size, padding, bias)[source]¶
Bases:
LightningModule3D convolution with batch normalization from Theo Glauch’s DNN.
Create a Conv3dBN module.
- Parameters:
in_channels (
int) – Number of input channels.out_channels (
int) – Number of output channels.kernel_size (
Tuple[int,int,int]) – Size of the kernel.padding (
Union[str,Tuple[int,int,int]]) – Padding of the kernel.bias (
bool, default:False) – If True, bias is used in the Convolution.
- class graphnet.models.components.cnn_convolutions.InceptionBlock4(in_channels, out_channels, t0, t1, t2, n_pool)[source]¶
Bases:
LightningModuleInception block with 4 parallel towers from Theo Glauch’s DNN.
Create a InceptionBlock4 module.
- Parameters:
in_channels (
int) – Number of input channels.out_channels (
int) – Number of output channels.t0 (
int, default:2) – Size of the first kernel sequence.t1 (
int, default:4) – Size of the second kernel sequence.t2 (
int, default:5) – Size of the third kernel sequence.n_pool (
int, default:3) – Size of the pooling kernel.
- class graphnet.models.components.cnn_convolutions.InceptionResnet(in_channels, out_channels, t1, t2, n_pool, scale)[source]¶
Bases:
LightningModuleInception block with residual connections from Theo Glauch’s DNN.
Create a InceptionResnet module.
- Parameters:
in_channels (
int) – Number of input channels.out_channels (
int) – Number of output channels.t1 (
int, default:2) – Size of the first kernel sequence.t2 (
int, default:4) – Size of the second kernel sequence.n_pool (
int, default:3) – Size of the pooling kernel.scale (
float, default:0.1) – Scaling factor for the residual connection.