ILA Class Reference

Inheritance diagram for ILA:

Collaboration diagram for ILA:

Public Member Functions
def	__init__ (self, chan, chan_out=None, kernel_size=1, padding=0, stride=1, key_dim=32, value_dim=64, heads=8, norm_queries=True)
	Constructor.
def	forward (self, x, context=None)
	forward

Public Attributes
	chan
	chan
	key_dim
	key dimension
	value_dim
	value dimension
	heads
	heads
	norm_queries
	norm queries
	to_q
	q
	to_k
	k
	to_v
	v
	to_out
	to out

Detailed Description

Image_Linear_Attention

Definition at line 607 of file ieagan.py.

Constructor & Destructor Documentation

__init__()

def __init__	(		self,
			chan,
			chan_out = None,
			kernel_size = 1,
			padding = 0,
			stride = 1,
			key_dim = 32,
			value_dim = 64,
			heads = 8,
			norm_queries = True
	)

Constructor.

Definition at line 613 of file ieagan.py.

    ):
        super().__init__()
        
        self.chan = chan
        chan_out = chan if chan_out is None else chan_out
 
        
        self.key_dim = key_dim
        
        self.value_dim = value_dim
        
        self.heads = heads
 
        
        self.norm_queries = norm_queries
 
        conv_kwargs = {"padding": padding, "stride": stride}
        
        self.to_q = nn.Conv2d(chan, key_dim * heads, kernel_size, **conv_kwargs)
        
        self.to_k = nn.Conv2d(chan, key_dim * heads, kernel_size, **conv_kwargs)
        
        self.to_v = nn.Conv2d(chan, value_dim * heads, kernel_size, **conv_kwargs)
 
        out_conv_kwargs = {"padding": padding}
        
        self.to_out = nn.Conv2d(
            value_dim * heads, chan_out, kernel_size, **out_conv_kwargs
        )
 

Member Function Documentation

forward()

def forward	(		self,
			x,
			context = None
	)

forward

Definition at line 655 of file ieagan.py.

    def forward(self, x, context=None):
        b, c, h, w, k_dim, heads = *x.shape, self.key_dim, self.heads
 
        q, k, v = (self.to_q(x), self.to_k(x), self.to_v(x))
 
        q, k, v = map(lambda t: t.reshape(b, heads, -1, h * w), (q, k, v))
 
        q, k = map(lambda x: x * (self.key_dim**-0.25), (q, k))
 
        if context is not None:
            context = context.reshape(b, c, 1, -1)
            ck, cv = self.to_k(context), self.to_v(context)
            ck, cv = map(lambda t: t.reshape(b, heads, k_dim, -1), (ck, cv))
            k = torch.cat((k, ck), dim=3)
            v = torch.cat((v, cv), dim=3)
 
        k = k.softmax(dim=-1)
 
        if self.norm_queries:
            q = q.softmax(dim=-2)
 
        context = torch.einsum("bhdn,bhen->bhde", k, v)
        out = torch.einsum("bhdn,bhde->bhen", q, context)
        out = out.reshape(b, -1, h, w)
        out = self.to_out(out)
        return out
 
 

Member Data Documentation

chan

chan

chan

Definition at line 627 of file ieagan.py.

heads

heads

heads

Definition at line 635 of file ieagan.py.

key_dim

key_dim

key dimension

Definition at line 631 of file ieagan.py.

norm_queries

norm_queries

norm queries

Definition at line 638 of file ieagan.py.

to_k

to_k

k

Definition at line 644 of file ieagan.py.

to_out

to_out

to out

Definition at line 650 of file ieagan.py.

to_q

to_q

q

Definition at line 642 of file ieagan.py.

to_v

to_v

v

Definition at line 646 of file ieagan.py.

value_dim

value_dim

value dimension

Definition at line 633 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