ccbn Class Reference

Inheritance diagram for ccbn:

Public Member Functions
def	__init__ (self, output_size, input_size, which_linear, eps=1e-5, momentum=0.1, cross_replica=False, mybn=False, norm_style="bn")
	Constructor.
def	forward (self, x, y)
	forward
def	extra_repr (self)
	extra_repr

Public Attributes
	input_size
	output size
	gain
	Prepare gain and bias layers.
	bias
	bias
	eps
	epsilon to avoid dividing by 0
	momentum
	Momentum.
	cross_replica
	Use cross-replica batchnorm?
	mybn
	Use my batchnorm?
	norm_style
	Norm style?
	bn
	bn

Detailed Description

Class-conditional bn
output size is the number of channels, input size is for the linear layers
Andy's Note: this class feels messy but I'm not really sure how to clean it up  # noqa
Suggestions welcome! (By which I mean, refactor this and make a merge request
if you want to make this more readable/usable).

Definition at line 516 of file ieagan.py.

Constructor & Destructor Documentation

__init__()

def __init__	(		self,
			output_size,
			input_size,
			which_linear,
			eps = 1e-5,
			momentum = 0.1,
			cross_replica = False,
			mybn = False,
			norm_style = "bn"
	)

Constructor.

Definition at line 526 of file ieagan.py.

    ):
        super(ccbn, self).__init__()
        
        self.output_size, self.input_size = output_size, input_size
        
        self.gain = which_linear(input_size, output_size)
        
        self.bias = which_linear(input_size, output_size)
        
        self.eps = eps
        
        self.momentum = momentum
        
        self.cross_replica = cross_replica
        
        self.mybn = mybn
        
        self.norm_style = norm_style
 
        if self.mybn:
            
            self.bn = myBN(output_size, self.eps, self.momentum)
        elif self.norm_style in ["bn", "in"]:
            self.register_buffer("stored_mean", torch.zeros(output_size))
            self.register_buffer("stored_var", torch.ones(output_size))
 

Member Function Documentation

extra_repr()

def extra_repr

(

self

)

extra_repr

Definition at line 601 of file ieagan.py.

    def extra_repr(self):
        s = "out: {output_size}, in: {input_size},"
        s += " cross_replica={cross_replica}"
        return s.format(**self.__dict__)
 
 

forward()

def forward	(		self,
			x,
			y
	)

forward

Definition at line 563 of file ieagan.py.

    def forward(self, x, y):
        # Calculate class-conditional gains and biases
        gain = (1 + self.gain(y)).view(y.size(0), -1, 1, 1)
        bias = self.bias(y).view(y.size(0), -1, 1, 1)
        # If using my batchnorm
        if self.mybn:
            return self.bn(x, gain=gain, bias=bias)
        # else:
        else:
            if self.norm_style == "bn":
                out = F.batch_norm(
                    x,
                    self.stored_mean,
                    self.stored_var,
                    None,
                    None,
                    self.training,
                    0.1,
                    self.eps,
                )
            elif self.norm_style == "in":
                out = F.instance_norm(
                    x,
                    self.stored_mean,
                    self.stored_var,
                    None,
                    None,
                    self.training,
                    0.1,
                    self.eps,
                )
            elif self.norm_style == "gn":
                out = groupnorm(x, self.normstyle)
            elif self.norm_style == "nonorm":
                out = x
            return out * gain + bias
 

Member Data Documentation

bias

bias

bias

Definition at line 543 of file ieagan.py.

bn

bn

bn

Definition at line 557 of file ieagan.py.

cross_replica

cross_replica

Use cross-replica batchnorm?

Definition at line 549 of file ieagan.py.

eps

eps

epsilon to avoid dividing by 0

Definition at line 545 of file ieagan.py.

gain

gain

Prepare gain and bias layers.

Definition at line 541 of file ieagan.py.

input_size

input_size

output size

Definition at line 539 of file ieagan.py.

momentum

momentum

Momentum.

Definition at line 547 of file ieagan.py.

mybn

mybn

Use my batchnorm?

Definition at line 551 of file ieagan.py.

norm_style

norm_style

Norm style?

Definition at line 553 of file ieagan.py.

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

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