File size: 2,607 Bytes
6d923b7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 | import torch
import torch.nn as nn
import torch.nn.functional as F
class HandcraftedFilter(nn.Module):
def __init__(self, feat_dim, num_relations):
super().__init__()
self.num_relations = num_relations
self.filter_weights = torch.tensor([1 / 4, 1 / 2, 1, 1 / 2, 1 / 4],
dtype=torch.float32)
self.expanded_filter_weights = self.filter_weights.view(
1, 1, -1).repeat(feat_dim, 1, 1)
self.fc1 = nn.Linear(feat_dim, feat_dim // 2)
self.fc2 = nn.Linear(feat_dim // 2, feat_dim // 4)
self.span_head = nn.Linear(feat_dim // 4, num_relations)
self.pred_head = nn.Linear(feat_dim // 4, num_relations)
def forward(self, x):
_, _, feat_dim = x.shape
x = x.permute(0, 2, 1)
filtered_x = F.conv1d(x,
self.expanded_filter_weights.to(x.device),
padding=2,
groups=feat_dim)
filtered_x = filtered_x.permute(0, 2, 1)
filtered_x = F.relu(self.fc1(filtered_x))
filtered_x = F.relu(self.fc2(filtered_x))
span_pred = self.span_head(filtered_x)
relation_pred = self.pred_head(filtered_x)
relation_pred = torch.max(relation_pred, dim=1).values
return span_pred, relation_pred
class Learnable1DConv(nn.Module):
def __init__(self, input_dim, num_relations, kernel_size=5, num_layers=1):
super(Learnable1DConv, self).__init__()
self.num_relations = num_relations
layers = []
for _ in range(num_layers):
layers.append(
nn.Conv1d(input_dim,
input_dim,
kernel_size,
padding=kernel_size // 2))
layers.append(nn.ReLU())
self.conv_layers = nn.Sequential(*layers)
self.fc1 = nn.Linear(input_dim, input_dim // 2)
self.fc2 = nn.Linear(input_dim // 2, input_dim // 4)
self.span_head = nn.Linear(input_dim // 4, num_relations)
self.pred_head = nn.Linear(input_dim // 4, num_relations)
def forward(self, x):
x = x.permute(0, 2, 1)
conv_output = self.conv_layers(x)
filtered_x = conv_output.permute(0, 2, 1)
filtered_x = F.relu(self.fc1(filtered_x))
filtered_x = F.relu(self.fc2(filtered_x))
span_pred = self.span_head(filtered_x)
relation_pred = self.pred_head(filtered_x)
relation_pred = torch.max(relation_pred, dim=1).values
return span_pred, relation_pred
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