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import torch
from torch import nn
class FeedForward(nn.Module):
def __init__(self, dim, hidden_dim, dropout):
super().__init__()
self.net = nn.Sequential(
nn.Linear(dim, hidden_dim),
nn.GELU(),
nn.Dropout(dropout),
nn.Linear(hidden_dim, dim),
nn.Dropout(dropout)
)
def forward(self, x):
return self.net(x)
class ContextualizerBlock(nn.Module):
def __init__(self, d_model,d_ffn,dropout,num_tokens):
super().__init__()
self.context_proj = nn.Linear(d_model,d_model)
self.mlp = FeedForward(d_model,d_ffn,dropout)
self.norm = nn.LayerNorm(d_model)
self.upsample = nn.Upsample(scale_factor=num_tokens,mode='nearest')
self.downsample = nn.Upsample(scale_factor= 1/num_tokens, mode='nearest')
def forward(self, x):
res = x
x = self.norm(x)
context = x
dim0 = context.shape[0]
dim1 = context.shape[1]
dim2 = context.shape[2]
context = context.reshape([dim0,1,dim1*dim2])
context = self.downsample(context)
context = context.reshape([dim0,dim2])
context = self.context_proj(context)
context = context.reshape([dim0,1,dim2])
context = self.upsample(context)
context = context.reshape([dim0,dim1,dim2])
x = context
x = x + res
res = x
x = self.norm(x)
x = self.mlp(x)
out = x + res
return out
return
class Contextualizer(nn.Module):
def __init__(self, d_model, d_ffn, num_layers,dropout,num_tokens):
super().__init__()
self.model = nn.Sequential(
*[ContextualizerBlock(d_model,d_ffn,dropout,num_tokens) for _ in range(num_layers)],
)
def forward(self, x):
x = self.model(x)
return x
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