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import torch
import torch.nn as nn
from model.util import TransformerWithToken
class LayoutNet(nn.Module):
def __init__(self, num_label):
super().__init__()
d_model = 256
nhead = 4
num_layers = 4
max_bbox = 50
# encoder
self.emb_label = nn.Embedding(num_label, d_model)
self.fc_bbox = nn.Linear(4, d_model)
self.enc_fc_in = nn.Linear(d_model * 2, d_model)
self.enc_transformer = TransformerWithToken(d_model=d_model,
dim_feedforward=d_model // 2,
nhead=nhead, num_layers=num_layers)
self.fc_out_disc = nn.Linear(d_model, 1)
# decoder
self.pos_token = nn.Parameter(torch.rand(max_bbox, 1, d_model))
self.dec_fc_in = nn.Linear(d_model * 2, d_model)
te = nn.TransformerEncoderLayer(d_model=d_model, nhead=nhead,
dim_feedforward=d_model // 2)
self.dec_transformer = nn.TransformerEncoder(te, num_layers=num_layers)
self.fc_out_cls = nn.Linear(d_model, num_label)
self.fc_out_bbox = nn.Linear(d_model, 4)
def extract_features(self, bbox, label, padding_mask):
b = self.fc_bbox(bbox)
l = self.emb_label(label)
x = self.enc_fc_in(torch.cat([b, l], dim=-1))
x = torch.relu(x).permute(1, 0, 2)
x = self.enc_transformer(x, padding_mask)
return x[0]
def forward(self, bbox, label, padding_mask):
B, N, _ = bbox.size()
x = self.extract_features(bbox, label, padding_mask)
logit_disc = self.fc_out_disc(x).squeeze(-1)
x = x.unsqueeze(0).expand(N, -1, -1)
t = self.pos_token[:N].expand(-1, B, -1)
x = torch.cat([x, t], dim=-1)
x = torch.relu(self.dec_fc_in(x))
x = self.dec_transformer(x, src_key_padding_mask=padding_mask)
x = x.permute(1, 0, 2)[~padding_mask]
# logit_cls: [M, L] bbox_pred: [M, 4]
logit_cls = self.fc_out_cls(x)
bbox_pred = torch.sigmoid(self.fc_out_bbox(x))
return logit_disc, logit_cls, bbox_pred
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