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base_model.py
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base_model.py
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"""
This code is developed based on Jin-Hwa Kim's repository (Bilinear Attention Networks - https://github.com/jnhwkim/ban-vqa) by Xuan B. Nguyen
"""
import torch
import torch.nn as nn
from attention import BiAttention, StackedAttention
from language_model import WordEmbedding, QuestionEmbedding
from classifier import SimpleClassifier
from fc import FCNet
from bc import BCNet
from counting import Counter
from utils import tfidf_loading
from simple_cnn import SimpleCNN, SimpleCNN32
from learner import MAML
from auto_encoder import Auto_Encoder_Model
# Create BAN model
class BAN_Model(nn.Module):
def __init__(self, dataset, w_emb, q_emb, v_att, b_net, q_prj, c_prj, classifier, counter, args, maml_v_emb, ae_v_emb):
super(BAN_Model, self).__init__()
self.args = args
self.dataset = dataset
self.op = args.op
self.glimpse = args.gamma
self.w_emb = w_emb
self.q_emb = q_emb
self.v_att = v_att
self.b_net = nn.ModuleList(b_net)
self.q_prj = nn.ModuleList(q_prj)
if counter is not None: # if do not use counter
self.c_prj = nn.ModuleList(c_prj)
self.classifier = classifier
self.counter = counter
self.drop = nn.Dropout(.5)
self.tanh = nn.Tanh()
if args.maml:
# init multiple maml models
if len(self.args.maml_nums) > 1:
self.maml_v_emb = nn.ModuleList(model for model in maml_v_emb)
else:
self.maml_v_emb = maml_v_emb
if args.autoencoder:
self.ae_v_emb = ae_v_emb
self.convert = nn.Linear(16384, args.feat_dim)
def forward(self, v, q):
"""Forward
v: [batch, num_objs, obj_dim]
b: [batch, num_objs, b_dim]
q: [batch_size, seq_length]
return: logits, not probs
"""
# get visual feature
if self.args.maml: # get maml feature
# compute multiple maml embeddings and concatenate them
if len(self.args.maml_nums) > 1:
maml_v_emb = self.maml_v_emb[0](v[0]).unsqueeze(1)
for j in range(1, len(self.maml_v_emb)):
maml_v_emb_temp = self.maml_v_emb[j](v[0]).unsqueeze(1)
maml_v_emb = torch.cat((maml_v_emb, maml_v_emb_temp), 2)
else:
maml_v_emb = self.maml_v_emb(v[0]).unsqueeze(1)
v_emb = maml_v_emb
if self.args.autoencoder: # get dae feature
encoder = self.ae_v_emb.forward_pass(v[1])
decoder = self.ae_v_emb.reconstruct_pass(encoder)
ae_v_emb = encoder.view(encoder.shape[0], -1)
ae_v_emb = self.convert(ae_v_emb).unsqueeze(1)
v_emb = ae_v_emb
if self.args.maml and self.args.autoencoder: # concatenate maml feature with dae feature
v_emb = torch.cat((maml_v_emb, ae_v_emb), 2)
# get lextual feature
w_emb = self.w_emb(q)
q_emb = self.q_emb.forward_all(w_emb) # [batch, q_len, q_dim]
# Attention
b_emb = [0] * self.glimpse
att, logits = self.v_att.forward_all(v_emb, q_emb) # b x g x v x q
for g in range(self.glimpse):
b_emb[g] = self.b_net[g].forward_with_weights(v_emb, q_emb, att[:,g,:,:]) # b x l x h
atten, _ = logits[:,g,:,:].max(2)
q_emb = self.q_prj[g](b_emb[g].unsqueeze(1)) + q_emb
if self.args.autoencoder:
return q_emb.sum(1), decoder
return q_emb.sum(1)
def classify(self, input_feats):
return self.classifier(input_feats)
# Create SAN model
class SAN_Model(nn.Module):
def __init__(self, w_emb, q_emb, v_att, classifier, args, maml_v_emb, ae_v_emb):
super(SAN_Model, self).__init__()
self.args = args
self.w_emb = w_emb
self.q_emb = q_emb
self.v_att = v_att
self.classifier = classifier
if args.maml:
# init multiple maml models
if len(self.args.maml_nums) > 1:
self.maml_v_emb = nn.ModuleList(model for model in maml_v_emb)
else:
self.maml_v_emb = maml_v_emb
if args.autoencoder:
self.ae_v_emb = ae_v_emb
self.convert = nn.Linear(16384, args.feat_dim)
def forward(self, v, q):
"""Forward
v: [batch, num_objs, obj_dim]
b: [batch, num_objs, b_dim]
q: [batch_size, seq_length]
return: logits, not probs
"""
# get visual feature
if self.args.maml: # get maml feature
# compute multiple maml embeddings and concatenate them
if len(self.args.maml_nums) > 1:
maml_v_emb = self.maml_v_emb[0](v[0]).unsqueeze(1)
for j in range(1, len(self.maml_v_emb)):
maml_v_emb_temp = self.maml_v_emb[j](v[0]).unsqueeze(1)
maml_v_emb = torch.cat((maml_v_emb, maml_v_emb_temp), 2)
else:
maml_v_emb = self.maml_v_emb(v[0]).unsqueeze(1)
v_emb = maml_v_emb
if self.args.autoencoder: # get dae feature
encoder = self.ae_v_emb.forward_pass(v[1])
decoder = self.ae_v_emb.reconstruct_pass(encoder)
ae_v_emb = encoder.view(encoder.shape[0], -1)
ae_v_emb = self.convert(ae_v_emb).unsqueeze(1)
v_emb = ae_v_emb
if self.args.maml and self.args.autoencoder: # concatenate maml feature with dae feature
v_emb = torch.cat((maml_v_emb, ae_v_emb), 2)
# get textual feature
w_emb = self.w_emb(q)
q_emb = self.q_emb(w_emb) # [batch, q_dim], return final hidden state
# Attention
att = self.v_att(v_emb, q_emb)
if self.args.autoencoder:
return att, decoder
return att
def classify(self, input_feats):
return self.classifier(input_feats)
# Build BAN model
def build_BAN(dataset, args, priotize_using_counter=False):
# init word embedding module, question embedding module, and Attention network
w_emb = WordEmbedding(dataset.dictionary.ntoken, 300, .0, args.op)
q_emb = QuestionEmbedding(300 if 'c' not in args.op else 600, args.num_hid, 1, False, .0, args.rnn)
v_att = BiAttention(dataset.v_dim, args.num_hid, args.num_hid, args.gamma)
# build and load pre-trained MAML model(s)
if args.maml:
# load multiple pre-trained maml models(s)
if len(args.maml_nums) > 1:
maml_v_emb = []
for model_t in args.maml_nums:
weight_path = args.VQA_dir + '/maml/' + 't%s_'%(model_t) + args.maml_model_path
print('load initial weights MAML from: %s' % (weight_path))
# maml_v_emb = SimpleCNN32(weight_path, args.eps_cnn, args.momentum_cnn)
maml_v_emb_temp = MAML(args.VQA_dir)
maml_v_emb_temp.load_state_dict(torch.load(weight_path))
maml_v_emb.append(maml_v_emb_temp)
else:
weight_path = args.VQA_dir + '/maml/' + 't%s_' % (args.maml_nums[0]) + args.maml_model_path
print('load initial weights MAML from: %s' % (weight_path))
# maml_v_emb = SimpleCNN32(weight_path, args.eps_cnn, args.momentum_cnn)
maml_v_emb = MAML(args.VQA_dir)
maml_v_emb.load_state_dict(torch.load(weight_path))
# build and load pre-trained Auto-encoder model
if args.autoencoder:
ae_v_emb = Auto_Encoder_Model()
weight_path = args.VQA_dir + '/' + args.ae_model_path
print('load initial weights DAE from: %s'%(weight_path))
ae_v_emb.load_state_dict(torch.load(weight_path))
# Loading tfidf weighted embedding
if hasattr(args, 'tfidf'):
w_emb = tfidf_loading(args.tfidf, w_emb, args)
# Optional module: counter for BAN
use_counter = args.use_counter if priotize_using_counter is None else priotize_using_counter
if use_counter or priotize_using_counter:
objects = 10 # minimum number of boxes
if use_counter or priotize_using_counter:
counter = Counter(objects)
else:
counter = None
# init BAN residual network
b_net = []
q_prj = []
c_prj = []
for i in range(args.gamma):
b_net.append(BCNet(dataset.v_dim, args.num_hid, args.num_hid, None, k=1))
q_prj.append(FCNet([args.num_hid, args.num_hid], '', .2))
if use_counter or priotize_using_counter:
c_prj.append(FCNet([objects + 1, args.num_hid], 'ReLU', .0))
# init classifier
classifier = SimpleClassifier(
args.num_hid, args.num_hid * 2, dataset.num_ans_candidates, args)
# contruct VQA model and return
if args.maml and args.autoencoder:
return BAN_Model(dataset, w_emb, q_emb, v_att, b_net, q_prj, c_prj, classifier, counter, args, maml_v_emb,
ae_v_emb)
elif args.maml:
return BAN_Model(dataset, w_emb, q_emb, v_att, b_net, q_prj, c_prj, classifier, counter, args, maml_v_emb,
None)
elif args.autoencoder:
return BAN_Model(dataset, w_emb, q_emb, v_att, b_net, q_prj, c_prj, classifier, counter, args, None,
ae_v_emb)
return BAN_Model(dataset, w_emb, q_emb, v_att, b_net, q_prj, c_prj, classifier, counter, args, None, None)
# Build SAN model
def build_SAN(dataset, args):
# init word embedding module, question embedding module, and Attention network
w_emb = WordEmbedding(dataset.dictionary.ntoken, 300, 0.0, args.op)
q_emb = QuestionEmbedding(300 if 'c' not in args.op else 600, args.num_hid, 1, False, 0.0, args.rnn)
v_att = StackedAttention(args.num_stacks, dataset.v_dim, args.num_hid, args.num_hid, dataset.num_ans_candidates,
args.dropout)
# build and load pre-trained MAML model(s)
if args.maml:
# load multiple pre-trained maml models(s)
if len(args.maml_nums) > 1:
maml_v_emb = []
for model_t in args.maml_nums:
weight_path = args.VQA_dir + '/maml/' + 't%s_'%(model_t) + args.maml_model_path
print('load initial weights MAML from: %s' % (weight_path))
# maml_v_emb = SimpleCNN32(weight_path, args.eps_cnn, args.momentum_cnn)
maml_v_emb_temp = MAML(args.VQA_dir)
maml_v_emb_temp.load_state_dict(torch.load(weight_path))
maml_v_emb.append(maml_v_emb_temp)
else:
weight_path = args.VQA_dir + '/maml/' + 't%s_' % (args.maml_nums[0]) + args.maml_model_path
print('load initial weights MAML from: %s' % (weight_path))
maml_v_emb = MAML(args.VQA_dir)
maml_v_emb.load_state_dict(torch.load(weight_path))
# build and load pre-trained Auto-encoder model
if args.autoencoder:
ae_v_emb = Auto_Encoder_Model()
weight_path = args.VQA_dir + '/' + args.ae_model_path
print('load initial weights DAE from: %s'%(weight_path))
ae_v_emb.load_state_dict(torch.load(weight_path))
# Loading tfidf weighted embedding
if hasattr(args, 'tfidf'):
w_emb = tfidf_loading(args.tfidf, w_emb, args)
# init classifier
classifier = SimpleClassifier(
args.num_hid, 2 * args.num_hid, dataset.num_ans_candidates, args)
# contruct VQA model and return
if args.maml and args.autoencoder:
return SAN_Model(w_emb, q_emb, v_att, classifier, args, maml_v_emb, ae_v_emb)
elif args.maml:
return SAN_Model(w_emb, q_emb, v_att, classifier, args, maml_v_emb, None)
elif args.autoencoder:
return SAN_Model(w_emb, q_emb, v_att, classifier, args, None, ae_v_emb)
return SAN_Model(w_emb, q_emb, v_att, classifier, args, None, None)