inferno.extensions.layers package

Submodules

inferno.extensions.layers.activations module

class inferno.extensions.layers.activations.SELU

Bases: torch.nn.modules.module.Module

forward(input)

static selu(x)

inferno.extensions.layers.convolutional module

class inferno.extensions.layers.convolutional.ConvActivation(in_channels, out_channels, kernel_size, dim, activation, stride=1, dilation=1, groups=None, depthwise=False, bias=True, deconv=False, initialization=None)

Bases: torch.nn.modules.module.Module

Convolutional layer with ‘SAME’ padding followed by an activation.

forward(input)

get_padding(kernel_size, dilation)

class inferno.extensions.layers.convolutional.ConvELU2D(in_channels, out_channels, kernel_size)

Bases: inferno.extensions.layers.convolutional.ConvActivation

2D Convolutional layer with ‘SAME’ padding, ELU and orthogonal weight initialization.

class inferno.extensions.layers.convolutional.ConvELU3D(in_channels, out_channels, kernel_size)

Bases: inferno.extensions.layers.convolutional.ConvActivation

3D Convolutional layer with ‘SAME’ padding, ELU and orthogonal weight initialization.

class inferno.extensions.layers.convolutional.ConvSigmoid2D(in_channels, out_channels, kernel_size)

Bases: inferno.extensions.layers.convolutional.ConvActivation

2D Convolutional layer with ‘SAME’ padding, Sigmoid and orthogonal weight initialization.

class inferno.extensions.layers.convolutional.ConvSigmoid3D(in_channels, out_channels, kernel_size)

Bases: inferno.extensions.layers.convolutional.ConvActivation

3D Convolutional layer with ‘SAME’ padding, Sigmoid and orthogonal weight initialization.

class inferno.extensions.layers.convolutional.DeconvELU2D(in_channels, out_channels, kernel_size=2)

Bases: inferno.extensions.layers.convolutional.ConvActivation

2D deconvolutional layer with ELU and orthogonal weight initialization.

class inferno.extensions.layers.convolutional.DeconvELU3D(in_channels, out_channels, kernel_size=2)

Bases: inferno.extensions.layers.convolutional.ConvActivation

3D deconvolutional layer with ELU and orthogonal weight initialization.

class inferno.extensions.layers.convolutional.StridedConvELU2D(in_channels, out_channels, kernel_size, stride=2)

Bases: inferno.extensions.layers.convolutional.ConvActivation

2D strided convolutional layer with ‘SAME’ padding, ELU and orthogonal weight initialization.

class inferno.extensions.layers.convolutional.StridedConvELU3D(in_channels, out_channels, kernel_size, stride=2)

Bases: inferno.extensions.layers.convolutional.ConvActivation

2D strided convolutional layer with ‘SAME’ padding, ELU and orthogonal weight initialization.

class inferno.extensions.layers.convolutional.DilatedConvELU2D(in_channels, out_channels, kernel_size, dilation=2)

Bases: inferno.extensions.layers.convolutional.ConvActivation

2D dilated convolutional layer with ‘SAME’ padding, ELU and orthogonal weight initialization.

class inferno.extensions.layers.convolutional.DilatedConvELU3D(in_channels, out_channels, kernel_size, dilation=2)

Bases: inferno.extensions.layers.convolutional.ConvActivation

3D dilated convolutional layer with ‘SAME’ padding, ELU and orthogonal weight initialization.

class inferno.extensions.layers.convolutional.Conv2D(in_channels, out_channels, kernel_size, dilation=1, activation=None)

Bases: inferno.extensions.layers.convolutional.ConvActivation

2D convolutional layer with same padding and orthogonal weight initialization. By default, this layer does not apply an activation function.

class inferno.extensions.layers.convolutional.Conv3D(in_channels, out_channels, kernel_size, dilation=1, activation=None)

Bases: inferno.extensions.layers.convolutional.ConvActivation

3D convolutional layer with same padding and orthogonal weight initialization. By default, this layer does not apply an activation function.

class inferno.extensions.layers.convolutional.BNReLUConv2D(in_channels, out_channels, kernel_size)

Bases: inferno.extensions.layers.convolutional.ConvActivation

2D BN-ReLU-Conv layer with ‘SAME’ padding and He weight initialization.

forward(input)

class inferno.extensions.layers.convolutional.BNReLUConv3D(in_channels, out_channels, kernel_size)

Bases: inferno.extensions.layers.convolutional.ConvActivation

3D BN-ReLU-Conv layer with ‘SAME’ padding and He weight initialization.

forward(input)

class inferno.extensions.layers.convolutional.BNReLUDepthwiseConv2D(in_channels, out_channels, kernel_size)

Bases: inferno.extensions.layers.convolutional.ConvActivation

2D BN-ReLU-Conv layer with ‘SAME’ padding, He weight initialization and depthwise convolution. Note that depthwise convolutions require in_channels == out_channels.

forward(input)

class inferno.extensions.layers.convolutional.ConvSELU2D(in_channels, out_channels, kernel_size)

Bases: inferno.extensions.layers.convolutional.ConvActivation

2D Convolutional layer with SELU activation and the appropriate weight initialization.

class inferno.extensions.layers.convolutional.ConvSELU3D(in_channels, out_channels, kernel_size)

Bases: inferno.extensions.layers.convolutional.ConvActivation

3D Convolutional layer with SELU activation and the appropriate weight initialization.

inferno.extensions.layers.device module

class inferno.extensions.layers.device.DeviceTransfer(target_device, device_ordinal=None, async=False)

Bases: torch.nn.modules.module.Module

Layer to transfer variables to a specified device.

forward(*inputs)

class inferno.extensions.layers.device.OnDevice(module, target_device, device_ordinal=None, async=False)

Bases: torch.nn.modules.module.Module

Moves a module to a device. The advantage of using this over torch.nn.Module.cuda is that the inputs are transferred to the same device as the module, enabling easy model parallelism.

forward(*inputs)

transfer_module(module)

inferno.extensions.layers.reshape module

class inferno.extensions.layers.reshape.View(as_shape)

Bases: torch.nn.modules.module.Module

forward(input)

validate_as_shape(as_shape)

class inferno.extensions.layers.reshape.AsMatrix

Bases: inferno.extensions.layers.reshape.View

class inferno.extensions.layers.reshape.Flatten

Bases: inferno.extensions.layers.reshape.View

class inferno.extensions.layers.reshape.As3D(channel_as_z=False, num_channels_or_num_z_slices=1)

Bases: torch.nn.modules.module.Module

forward(input)

class inferno.extensions.layers.reshape.As2D(z_as_channel=True)

Bases: torch.nn.modules.module.Module

forward(input)

class inferno.extensions.layers.reshape.Concatenate(dim=1)

Bases: torch.nn.modules.module.Module

Concatenate input tensors along a specified dimension.

forward(*inputs)

class inferno.extensions.layers.reshape.Cat(dim=1)

Bases: inferno.extensions.layers.reshape.Concatenate

An alias for Concatenate. Hey, everyone knows who Cat is.

class inferno.extensions.layers.reshape.ResizeAndConcatenate(target_size, pool_mode='average')

Bases: torch.nn.modules.module.Module

Resize input tensors spatially (to a specified target size) before concatenating them along the channel dimension. The downsampling mode can be specified (‘average’ or ‘max’), but the upsampling is always ‘nearest’.

POOL_MODE_MAPPING = {'avg': 'avg', 'average': 'avg', 'mean': 'avg', 'max': 'max'}

forward(*inputs)

class inferno.extensions.layers.reshape.PoolCat(target_size, pool_mode='average')

Bases: inferno.extensions.layers.reshape.ResizeAndConcatenate

Alias for ResizeAndConcatenate, just to annoy snarky web developers.

class inferno.extensions.layers.reshape.Sum

Bases: torch.nn.modules.module.Module

Sum all inputs.

forward(*inputs)

class inferno.extensions.layers.reshape.SplitChannels(channel_index)

Bases: torch.nn.modules.module.Module

Split input at a given index along the channel axis.

forward(input)

Module contents