ResidualBlock Class Reference

Class for the residual block layer. More...

Inheritance diagram for ResidualBlock:

Public Member Functions
	__init__ (self, ninput, noutput, upsample=True)
	Constructor to create a new residual block layer.
	forward (self, x)
	Function to perform a forward pass.

Public Attributes
	upsample = upsample
	Whether to double the height and width of input.
	conv = None
	Convolutional layer in the shortcut branch.
	norm1 = nn.BatchNorm2d(ninput)
	First batch normalization layer in the residual branch.
	conv1 = nn.Conv2d(ninput, noutput, 3, 1, 1)
	First convolutional layer in the residual branch.
	norm2 = nn.BatchNorm2d(noutput)
	Second batch normalization layer in the residual branch.
	conv2 = nn.Conv2d(noutput, noutput, 3, 1, 1)
	Second convolutional layer in the residual branch.

Detailed Description

Class for the residual block layer.

Residual block layer.

Definition at line 19 of file resnet.py.

Constructor & Destructor Documentation

__init__()

__init__	(		self,
			ninput,
			noutput,
			upsample = True )

Constructor to create a new residual block layer.

Definition at line 24 of file resnet.py.

    def __init__(self, ninput, noutput, upsample=True):
        super().__init__()
        self.upsample = upsample
        # shortcut branch
        self.conv = None
        if upsample or (ninput != noutput):
            self.conv = nn.Conv2d(ninput, noutput, 1, 1, 0)
        # residual branch
        self.norm1 = nn.BatchNorm2d(ninput)
        self.conv1 = nn.Conv2d(ninput, noutput, 3, 1, 1)
        self.norm2 = nn.BatchNorm2d(noutput)
        self.conv2 = nn.Conv2d(noutput, noutput, 3, 1, 1)
 

Member Function Documentation

forward()

forward	(		self,
			x )

Function to perform a forward pass.

Compute the layer output for a given input.

Definition at line 39 of file resnet.py.

    def forward(self, x):
        """Compute the layer output for a given input."""
        # residual branch
        h = x
        h = self.norm1(h)
        h.relu_()
        if self.upsample:
            h = F.interpolate(h, mode="nearest", scale_factor=2)
        h = self.conv1(h)
        h = self.norm2(h)
        h.relu_()
        h = self.conv2(h)
        # shortcut branch
        if self.upsample:
            x = F.interpolate(x, mode="nearest", scale_factor=2)
        if self.conv:
            x = self.conv(x)
        # return sum of both
        return h + x
 

Member Data Documentation

conv

conv = None

Convolutional layer in the shortcut branch.

Definition at line 28 of file resnet.py.

conv1

conv1 = nn.Conv2d(ninput, noutput, 3, 1, 1)

First convolutional layer in the residual branch.

Definition at line 33 of file resnet.py.

conv2

conv2 = nn.Conv2d(noutput, noutput, 3, 1, 1)

Second convolutional layer in the residual branch.

Definition at line 35 of file resnet.py.

norm1

norm1 = nn.BatchNorm2d(ninput)

First batch normalization layer in the residual branch.

Definition at line 32 of file resnet.py.

norm2

norm2 = nn.BatchNorm2d(noutput)

Second batch normalization layer in the residual branch.

Definition at line 34 of file resnet.py.

upsample

upsample = upsample

Whether to double the height and width of input.

Definition at line 26 of file resnet.py.

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The documentation for this class was generated from the following file:

• pxd/scripts/pxd/background_generator/models/resnet.py