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import torch
import numpy as np
import copy
import torch.nn.functional as F
from torch.nn import Parameter
class HDReason(torch.nn.Module):
def __init__(self, d=10, D=256):
super().__init__()
self.d = d
self.D = D
self.vertex_d = 64
self.q_proj = torch.nn.Linear(self.d, self.vertex_d)
self.k_proj = torch.nn.Linear(self.d, self.vertex_d)
self.v_proj = torch.nn.Linear(self.d, self.vertex_d)
self.HDC_encoder = torch.nn.Linear(self.vertex_d, self.D)
self.HDC_encoder.requires_grad = False
self.Linear = torch.nn.Linear(d, self.vertex_d)
self.scale = self.D ** -0.5
#TODO: May need to chaneg
self.activation0 = torch.nn.ReLU()
self.activation1 = torch.nn.ReLU()
def forward(self, x):
#NOTE: build adjacency graph
q = self.activation1(self.HDC_encoder(self.activation0(self.q_proj(x))))
k = self.activation1(self.HDC_encoder(self.activation0(self.k_proj(x))))
q = q * self.scale
adj = q @ k.transpose(-2, -1)
adj = adj.softmax(dim=-1)
#NOTE: vertex hypervector
v = self.activation1(self.HDC_encoder(self.activation0(self.v_proj(x))))
#NOTE: GrapHD memorization
out = adj @ v
out = out*0.3 + 0.7*self.HDC_encoder(self.activation0(self.Linear(x)))
return out
class ScoreFunctionHDC(torch.nn.Module):
def __init__(self, N_words=20, HDV_D=512) -> None:
super().__init__()
self.D = HDV_D
self.N_words = N_words
self.norm = torch.nn.LayerNorm(self.D)
self.HDReason = HDReason(d=self.N_words, D=self.D)
self.Linear2 = torch.nn.Linear(self.D, self.D // 2)
self.Linear3 = torch.nn.Linear(self.D // 2, self.D // 8)
self.Linear4 = torch.nn.Linear(self.D // 8, 1)
self.Activation1 = torch.nn.ReLU()
self.Activation2 = torch.nn.Sigmoid()
self.register_parameter('bias',Parameter(torch.zeros(1)))
def forward(self, x):
#NOTE: input has shape NxN_word
#NOTE: N_bbox x N_word
output = self.HDReason(x)
output = self.norm(output)
output = self.Activation1(output)
output = self.Linear2(output)
output = self.Activation1(output)
output = self.Linear3(output)
output = self.Activation1(output)
output = self.Linear4(output) + self.bias
output = self.Activation2(output)
return output |