def build_baseline0_newatt(dataset, num_hid):
w_emb = WordEmbedding(dataset.question_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)
def build_baseline0(dataset):
w_emb = WordEmbedding(dataset.question_dictionary.ntoken, 300, 0.0)
q_emb = QuestionEmbedding(300, 1024, 1, False, 0.0)
v_att = Attention(dataset.v_dim, 1024, 1024)
q_net = FCNet([1024, 1024])
v_net = FCNet([dataset.v_dim, 1024])
classifier = SimpleClassifier(1024, 2 * 1024, dataset.num_ans_candidates,
0.5)
return BaseModel(w_emb, q_emb, v_att, q_net, v_net, classifier)
def visualize_vqe(dataset, num_hid, att_dim, dec_dim):
w_emb = WordEmbedding(dataset.question_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])
generator = STDecoder(
dataset.v_dim, num_hid, 300, dec_dim,\
dataset.explanation_dictionary.ntoken, 1, 0.5)
return VQE(w_emb, q_emb, v_att, q_net, v_net, generator)
def build_vqae_newatt(dataset, num_hid, att_dim, dec_dim):
w_emb = WordEmbedding(dataset.question_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)
generator = STDecoder(
dataset.v_dim, num_hid, 300, dec_dim,\
dataset.explanation_dictionary.ntoken, 1, 0.5)
return VQAE(w_emb, q_emb, v_att, q_net, v_net, classifier, generator)
def build_lstm_vqa(dataset, num_hid, att_dim, dec_dim):
w_emb = WordEmbedding(dataset.question_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])
generator = SATDecoder(
dataset.v_dim, num_hid, 300, att_dim, dec_dim,\
dataset.explanation_dictionary.ntoken, 1, 0.5)
#att_emb = nn.GRU(dataset.v_dim, num_hid, 1, False, batch_first=True)
att_emb = nn.GRUCell(dataset.v_dim, num_hid)
classifier = SimpleClassifier(num_hid, 2 * num_hid,
dataset.num_ans_candidates, 0.5)
return LSTM_VQA(w_emb, q_emb, v_att, q_net, v_net, generator, att_emb,
classifier)
def build_vqae2_newatt(dataset, num_hid, emb_rnn='GRU'):
w_emb = WordEmbedding(dataset.question_dictionary.ntoken, 300, 0.0)
q_emb = QuestionEmbedding(300, 1024, 1, False, 0.0)
v_att = NewAttention(dataset.v_dim, 1024, 1024)
q_net = FCNet([1024, 1024])
v_net = FCNet([dataset.v_dim, 1024])
classifier = SimpleClassifier(1024, 1024 * 2, dataset.num_ans_candidates,
0.5)
generator = STDecoder(
dataset.v_dim, 1024, 300, 1024,\
dataset.explanation_dictionary.ntoken, 1, 0.5)
e_emb = ExplainEmbedding(generator.embed, 300, num_hid, 1, False, 0.0,
emb_rnn)
e_net = FCNet([e_emb.num_hid, 1024])
return VQAE2(w_emb, q_emb, v_att, q_net, v_net, classifier, generator,
e_emb, e_net)
def build_vqae3_split(dataset, num_hid, att_dim, dec_dim):
w_emb = WordEmbedding(dataset.question_dictionary.ntoken, 300, 0.0)
q_emb = QuestionEmbedding(300, num_hid, 1, False, 0.0)
v_att_1 = NewAttention(dataset.v_dim, q_emb.num_hid, num_hid)
q_net_1 = FCNet([q_emb.num_hid, num_hid])
v_net_1 = FCNet([dataset.v_dim, num_hid])
v_att_2 = NewAttention(dataset.v_dim, q_emb.num_hid, num_hid)
q_net_2 = FCNet([q_emb.num_hid, num_hid])
v_net_2 = FCNet([dataset.v_dim, num_hid])
classifier = SimpleClassifier(num_hid, num_hid * 2,
dataset.num_ans_candidates, 0.5)
generator = STDecoder(
dataset.v_dim, num_hid, 300, dec_dim,\
dataset.explanation_dictionary.ntoken, 1, 0.5)
e_emb = ExplainEmbedding(generator.embed, 300, num_hid, 1, False, 0.0,
'GRU')
T_vq = FCNet([num_hid, num_hid])
T_e = FCNet([e_emb.num_hid, num_hid])
return Split_VQAE(w_emb, q_emb, v_att_1, q_net_1, v_net_1, v_att_2,
q_net_2, v_net_2, classifier, generator, e_emb, T_vq,
T_e)
