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import torch.nn as nn
import torch as th
from model.utils.nn_utils import LambdaModule
from einops import rearrange, repeat, reduce
class AlphaAttention(nn.Module):
def __init__(
self,
num_hidden,
num_objects,
heads,
dropout = 0.0,
need_weights = False
):
super(AlphaAttention, self).__init__()
self.to_sequence = LambdaModule(lambda x: rearrange(x, '(b o) c -> b o c', o = num_objects))
self.to_batch = LambdaModule(lambda x: rearrange(x, 'b o c -> (b o) c', o = num_objects))
self.alpha = nn.Parameter(th.zeros(1)+1e-12)
self.attention = nn.MultiheadAttention(
num_hidden,
heads,
dropout = dropout,
batch_first = True
)
self.need_weights = need_weights
self.att_weights = None
def forward(self, x: th.Tensor):
x = self.to_sequence(x)
att, self.att_weights = self.attention(x, x, x, need_weights=self.need_weights)
x = x + self.alpha * att
return self.to_batch(x)
class InputEmbeding(nn.Module):
def __init__(self, num_inputs, num_hidden):
super(InputEmbeding, self).__init__()
self.embeding = nn.Sequential(
nn.ReLU(),
nn.Linear(num_inputs, num_hidden),
nn.ReLU(),
nn.Linear(num_hidden, num_hidden),
)
self.skip = LambdaModule(
lambda x: repeat(x, 'b c -> b (n c)', n = num_hidden // num_inputs)
)
self.alpha = nn.Parameter(th.zeros(1)+1e-12)
def forward(self, input: th.Tensor):
return self.skip(input) + self.alpha * self.embeding(input)
class OutputEmbeding(nn.Module):
def __init__(self, num_hidden, num_outputs):
super(OutputEmbeding, self).__init__()
self.embeding = nn.Sequential(
nn.ReLU(),
nn.Linear(num_hidden, num_outputs),
nn.ReLU(),
nn.Linear(num_outputs, num_outputs),
)
self.skip = LambdaModule(
lambda x: reduce(x, 'b (n c) -> b c', 'mean', n = num_hidden // num_outputs)
)
self.alpha = nn.Parameter(th.zeros(1)+1e-12)
def forward(self, input: th.Tensor):
return self.skip(input) + self.alpha * self.embeding(input)
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