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import torch
import torch.nn as nn
class Adapter(nn.Module):
def __init__(self, D_features, mlp_ratio=0.25, act_layer=nn.GELU, skip_connect=True):
super().__init__()
self.skip_connect = skip_connect
D_hidden_features = int(D_features * mlp_ratio)
self.act = act_layer()
self.D_fc1 = nn.Linear(D_features, D_hidden_features)
self.D_fc2 = nn.Linear(D_hidden_features, D_features)
def forward(self, x):
# x is (BT, HW+1, D)
xs = self.D_fc1(x)
xs = self.act(xs)
xs = self.D_fc2(xs)
if self.skip_connect:
x = x + xs
else:
x = xs
return x |