inferno.extensions.layers package

Submodules

inferno.extensions.layers.activations module

class inferno.extensions.layers.activations.SELU

Bases: torch.nn.modules.module.Module

forward(input)

Defines the computation performed at every call.

Should be overriden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

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)

Defines the computation performed at every call.

Should be overriden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

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)

Defines the computation performed at every call.

Should be overriden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

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)

Defines the computation performed at every call.

Should be overriden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

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)

Defines the computation performed at every call.

Should be overriden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

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)

Defines the computation performed at every call.

Should be overriden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

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)

Defines the computation performed at every call.

Should be overriden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

transfer_module(module)

inferno.extensions.layers.reshape module

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

Bases: torch.nn.modules.module.Module

forward(input)

Defines the computation performed at every call.

Should be overriden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

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)

Defines the computation performed at every call.

Should be overriden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

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

Bases: torch.nn.modules.module.Module

forward(input)

Defines the computation performed at every call.

Should be overriden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

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

Bases: torch.nn.modules.module.Module

Concatenate input tensors along a specified dimension.

forward(*inputs)

Defines the computation performed at every call.

Should be overriden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

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

forward(*inputs)

Defines the computation performed at every call.

Should be overriden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

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)

Defines the computation performed at every call.

Should be overriden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

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)

Defines the computation performed at every call.

Should be overriden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

Module contents