`trw.simple_layers.simple_layers_implementations`¶

Module Contents¶

Classes¶

`Input`	Represent an input (i.e., a feature) to a network
`OutputClassification`	Output class for classification
`OutputEmbedding`	Create an embedding for display purposes
`ReLU`	Generic module
`BatchNorm2d`	Generic module
`BatchNorm3d`	Generic module
`_Reshape`	Base class for all neural network modules.
`Reshape`	Reshape a tensor to another shape
`Linear`	Generic module
`Flatten`	Generic module
`Conv2d`	Generic module
`Conv3d`	Generic module
`MaxPool2d`	Generic module
`MaxPool3d`	Generic module
`ConcatChannels`	Implement a channel concatenation layer

Functions¶

`return_output`(outputs, batch)
`_conv_2d_shape_fn`(node, module_args)
`_conv_3d_shape_fn`(node, module_args)

class trw.simple_layers.simple_layers_implementations.Input(shape: list, feature_name: str)¶

Bases: trw.simple_layers.simple_layers.SimpleLayerBase

Represent an input (i.e., a feature) to a network

get_module(self)¶: Return a nn.Module

class trw.simple_layers.simple_layers_implementations.OutputClassification(node, output_name, classes_name, **kwargs)¶

Bases: trw.simple_layers.simple_layers.SimpleOutputBase

Output class for classification

forward(self, inputs, batch)¶

Create a trw.train.Output from the inputs

Parameters

inputs – a list of inputs of the output node
batch – the batch of data fed to the network

Returns

a trw.train.Output object

get_module(self)¶: Return a nn.Module

trw.simple_layers.simple_layers_implementations.return_output(outputs, batch)¶

class trw.simple_layers.simple_layers_implementations.OutputEmbedding(node, output_name, functor=None)¶

Bases: trw.simple_layers.simple_layers.SimpleOutputBase

Create an embedding for display purposes

forward(self, inputs, batch)¶

Create a trw.train.Output from the inputs

Parameters

inputs – a list of inputs of the output node
batch – the batch of data fed to the network

Returns

a trw.train.Output object

get_module(self)¶: Return a nn.Module

class trw.simple_layers.simple_layers_implementations.ReLU(node)¶

Bases: trw.simple_layers.simple_layers.SimpleModule

Generic module

Module must have a single input and all the module’s parameters should be on the same device.

class trw.simple_layers.simple_layers_implementations.BatchNorm2d(node, eps=1e-05, momentum=0.1, affine=True)¶

Bases: trw.simple_layers.simple_layers.SimpleModule

Generic module

Module must have a single input and all the module’s parameters should be on the same device.

class trw.simple_layers.simple_layers_implementations.BatchNorm3d(node, eps=1e-05, momentum=0.1, affine=True)¶

Bases: trw.simple_layers.simple_layers.SimpleModule

Generic module

Module must have a single input and all the module’s parameters should be on the same device.

class trw.simple_layers.simple_layers_implementations._Reshape(shape)¶

Bases: torch.nn.Module

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing to nest them in a tree structure. You can assign the submodules as regular attributes:

import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will have their parameters converted too when you call to(), etc.

Variables: training (bool) – Boolean represents whether this module is in training or evaluation mode.

forward(self, x)¶

class trw.simple_layers.simple_layers_implementations.Reshape(node, shape)¶

Bases: trw.simple_layers.simple_layers.SimpleModule

Reshape a tensor to another shape

class trw.simple_layers.simple_layers_implementations.Linear(node, out_features)¶

Bases: trw.simple_layers.simple_layers.SimpleModule

Generic module

Module must have a single input and all the module’s parameters should be on the same device.

class trw.simple_layers.simple_layers_implementations.Flatten(node)¶

Bases: trw.simple_layers.simple_layers.SimpleModule

Generic module

Module must have a single input and all the module’s parameters should be on the same device.

trw.simple_layers.simple_layers_implementations._conv_2d_shape_fn(node, module_args)¶

class trw.simple_layers.simple_layers_implementations.Conv2d(node, out_channels, kernel_size, stride=1, padding='same')¶

Bases: trw.simple_layers.simple_layers.SimpleModule

Generic module

Module must have a single input and all the module’s parameters should be on the same device.

trw.simple_layers.simple_layers_implementations._conv_3d_shape_fn(node, module_args)¶

class trw.simple_layers.simple_layers_implementations.Conv3d(node, out_channels, kernel_size, stride=1, padding='same')¶

Bases: trw.simple_layers.simple_layers.SimpleModule

Generic module

Module must have a single input and all the module’s parameters should be on the same device.

class trw.simple_layers.simple_layers_implementations.MaxPool2d(node, kernel_size, stride=None)¶

Bases: trw.simple_layers.simple_layers.SimpleModule

Generic module

Module must have a single input and all the module’s parameters should be on the same device.

class trw.simple_layers.simple_layers_implementations.MaxPool3d(node, kernel_size, stride=None)¶

Bases: trw.simple_layers.simple_layers.SimpleModule

Generic module

Module must have a single input and all the module’s parameters should be on the same device.

class trw.simple_layers.simple_layers_implementations.ConcatChannels(nodes, flatten=False)¶

Bases: trw.simple_layers.simple_layers.SimpleMergeBase

Implement a channel concatenation layer

static calculate_shape(parents)¶

get_module(self)¶: Return a nn.Module

trw.simple_layers.simple_layers_implementations¶

Module Contents¶

Classes¶

Functions¶

`trw.simple_layers.simple_layers_implementations`¶