/
base_model.py
60 lines (49 loc) · 1.99 KB
/
base_model.py
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import torch
import torch.nn as nn
from attention import Attention, NewAttention
from language_model import WordEmbedding, QuestionEmbedding
from classifier import SimpleClassifier
from fc import FCNet
class BaseModel(nn.Module):
def __init__(self, w_emb, q_emb, v_att, q_net, v_net, classifier):
super(BaseModel, self).__init__()
self.w_emb = w_emb
self.q_emb = q_emb
self.v_att = v_att
self.q_net = q_net
self.v_net = v_net
self.classifier = classifier
def forward(self, v, b, q, labels):
"""Forward
v: [batch, num_objs, obj_dim]
b: [batch, num_objs, b_dim]
q: [batch_size, seq_length]
return: logits, not probs
"""
w_emb = self.w_emb(q)
q_emb = self.q_emb(w_emb) # [batch, q_dim]
att = self.v_att(v, q_emb)
v_emb = (att * v).sum(1) # [batch, v_dim]
q_repr = self.q_net(q_emb)
v_repr = self.v_net(v_emb)
joint_repr = q_repr * v_repr
logits = self.classifier(joint_repr)
return logits
def build_baseline0(dataset, num_hid):
w_emb = WordEmbedding(dataset.dictionary.ntoken, 300, 0.0)
q_emb = QuestionEmbedding(300, num_hid, 1, False, 0.0)
v_att = Attention(dataset.v_dim, q_emb.num_hid, num_hid)
q_net = FCNet([num_hid, num_hid])
v_net = FCNet([dataset.v_dim, num_hid])
classifier = SimpleClassifier(
num_hid, 2 * num_hid, dataset.num_ans_candidates, 0.5)
return BaseModel(w_emb, q_emb, v_att, q_net, v_net, classifier)
def build_baseline0_newatt(dataset, num_hid):
w_emb = WordEmbedding(dataset.dictionary.ntoken, 300, 0.0)
q_emb = QuestionEmbedding(300, num_hid, 1, False, 0.0)
v_att = NewAttention(dataset.v_dim, q_emb.num_hid, num_hid)
q_net = FCNet([q_emb.num_hid, num_hid])
v_net = FCNet([dataset.v_dim, num_hid])
classifier = SimpleClassifier(
num_hid, num_hid * 2, dataset.num_ans_candidates, 0.5)
return BaseModel(w_emb, q_emb, v_att, q_net, v_net, classifier)