SNConv2d Class Reference

Inheritance diagram for SNConv2d:

Public Member Functions
	__init__ (self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True, num_svs=1, num_itrs=1, eps=1e-12)
	Constructor.
	forward (self, x)
	forward
	u (self)
	sv (self)
	W_ (self)

Public Attributes
	bias
	stride
	padding
	dilation
	groups
	num_itrs = num_itrs
	Number of power iterations per step.
	num_svs = num_svs
	Number of singular values.
	transpose = transpose
	Transposed?
	eps = eps
	Epsilon value for avoiding divide-by-0.
	training

Detailed Description

2D Conv layer with spectral norm

Definition at line 284 of file ieagan.py.

Constructor & Destructor Documentation

__init__()

__init__	(		self,
			in_channels,
			out_channels,
			kernel_size,
			stride = 1,
			padding = 0,
			dilation = 1,
			groups = 1,
			bias = True,
			num_svs = 1,
			num_itrs = 1,
			eps = 1e-12 )

Constructor.

Definition at line 290 of file ieagan.py.

    ):
        nn.Conv2d.__init__(
            self,
            in_channels,
            out_channels,
            kernel_size,
            stride,
            padding,
            dilation,
            groups,
            bias,
        )
        SN.__init__(self, num_svs, num_itrs, out_channels, eps=eps)
 

Member Function Documentation

forward()

forward	(		self,
			x )

forward

Definition at line 318 of file ieagan.py.

    def forward(self, x):
        return F.conv2d(
            x,
            self.W_(),
            self.bias,
            self.stride,
            self.padding,
            self.dilation,
            self.groups,
        )
 
 

sv()

sv ( self )

inherited

Singular values
note that these buffers are just for logging and are not used in training.

Definition at line 256 of file ieagan.py.

    def sv(self):
        """
        Singular values
        note that these buffers are just for logging and are not used in training.
        """
        return [getattr(self, f"sv{i:d}") for i in range(self.num_svs)]
 

u()

u ( self )

inherited

Singular vectors (u side)

Definition at line 249 of file ieagan.py.

    def u(self):
        """
        Singular vectors (u side)
        """
        return [getattr(self, f"u{i:d}") for i in range(self.num_svs)]
 

W_()

W_ ( self )

inherited

Compute the spectrally-normalized weight

Definition at line 263 of file ieagan.py.

    def W_(self):
        """
        Compute the spectrally-normalized weight
        """
        W_mat = self.weight.view(self.weight.size(0), -1)
        if self.transpose:
            W_mat = W_mat.t()
        # Apply num_itrs power iterations
        for _ in range(self.num_itrs):
            svs, _, _ = power_iteration(
                W_mat, self.u, update=self.training, eps=self.eps
            )
            # Update the svs
        if self.training:
            # Make sure to do this in a no_grad() context or you'll get memory leaks!  # noqa
            with torch.no_grad():
                for i, sv in enumerate(svs):
                    self.sv[i][:] = sv
        return self.weight / svs[0]
 
 

Member Data Documentation

bias

bias

Definition at line 322 of file ieagan.py.

dilation

dilation

Definition at line 325 of file ieagan.py.

eps

eps = eps

inherited

Epsilon value for avoiding divide-by-0.

Definition at line 242 of file ieagan.py.

groups

groups

Definition at line 326 of file ieagan.py.

num_itrs

num_itrs = num_itrs

inherited

Number of power iterations per step.

Definition at line 236 of file ieagan.py.

num_svs

num_svs = num_svs

inherited

Number of singular values.

Definition at line 238 of file ieagan.py.

padding

padding

Definition at line 324 of file ieagan.py.

stride

stride

Definition at line 323 of file ieagan.py.

training

training

inherited

Initial value:

=  power_iteration(
                W_mat, self.u, update=self.training, eps=self.eps
            )

Definition at line 276 of file ieagan.py.

transpose

transpose = transpose

inherited

Transposed?

Definition at line 240 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