-
Notifications
You must be signed in to change notification settings - Fork 0
/
sans_model.py
169 lines (132 loc) · 7.43 KB
/
sans_model.py
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
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
import torch, pdb
import torch.nn as nn
import torchvision.models as models
from torch.autograd import Variable
class ImgEncoder(nn.Module):
def __init__(self, embed_size):
"""(1) Load the pretrained model as you want.
cf) one needs to check structure of model using 'print(model)'
to remove last fc layer from the model.
(2) Replace final fc layer (score values from the ImageNet)
with new fc layer (image feature).
(3) Normalize feature vector.
"""
super(ImgEncoder, self).__init__()
vgg_model = models.vgg19(pretrained=True)
self.in_features = 512 # input size of feature vector
model = nn.Sequential(
*list(vgg_model.features.children())[:-1]) # remove last fc layer
self.model = model # loaded model without last fc layer
self.fc = nn.Linear(512, embed_size) # feature vector of image
self.tanh = nn.Tanh()
self.dropout = nn.Dropout(0.5)
def forward(self, image):
"""Extract feature vector from image vector.
"""
with torch.no_grad():
img_feature = self.model(image) # [batch_size, vgg16(19)_fc=4096]
#print(img_feature.shape)
#pdb.set_trace()
img_feature = img_feature.view(-1, self.in_features).view(-1,196,512)
#print(img_feature.shape)
#pdb.set_trace()
img_feature = self.fc(img_feature) # [batch_size, embed_size]
#print(img_feature.shape)
#pdb.set_trace()
img_feature = self.dropout(self.tanh(img_feature))
return img_feature
class QstEncoder(nn.Module):
def __init__(self, qst_vocab_size, word_embed_size, embed_size, num_layers, hidden_size):
super(QstEncoder, self).__init__()
self.word2vec = nn.Embedding(qst_vocab_size, word_embed_size)
self.tanh = nn.Tanh()
self.lstm = nn.LSTM(word_embed_size, hidden_size, num_layers)
self.fc = nn.Linear(2*num_layers*hidden_size, embed_size) # 2 for hidden and cell states
def forward(self, question):
qst_vec = self.word2vec(question) # [batch_size, max_qst_length=30, word_embed_size=300]
qst_vec = self.tanh(qst_vec)
qst_vec = qst_vec.transpose(0, 1) # [max_qst_length=30, batch_size, word_embed_size=300]
self.lstm.flatten_parameters()
_, (hidden, cell) = self.lstm(qst_vec) # [num_layers=2, batch_size, hidden_size=512]
qst_feature = torch.cat((hidden, cell), 2) # [num_layers=2, batch_size, 2*hidden_size=1024]
qst_feature = qst_feature.transpose(0, 1) # [batch_size, num_layers=2, 2*hidden_size=1024]
qst_feature = qst_feature.reshape(qst_feature.size()[0], -1) # [batch_size, 2*num_layers*hidden_size=2048]
qst_feature = self.tanh(qst_feature)
qst_feature = self.fc(qst_feature) # [batch_size, embed_size]
return qst_feature
class Attention(nn.Module): # Extend PyTorch's Module class
def __init__(self, input_size, att_size, img_seq_size, output_size, drop_ratio):
super(Attention, self).__init__() # Must call super __init__()
self.input_size = input_size
self.att_size = att_size
self.img_seq_size = img_seq_size
self.output_size = output_size
self.drop_ratio = drop_ratio
self.tan = nn.Tanh()
self.dp = nn.Dropout(drop_ratio)
self.sf = nn.Softmax(dim=1)
self.fc1_1a = nn.Linear(input_size, 768, bias=True)
self.fc1_1b = nn.Linear(768, 512, bias=True)
self.fc1_2a = nn.Linear(input_size, 768, bias=False)
self.fc1_2b = nn.Linear(768, 512, bias=False)
self.fc1_3a = nn.Linear(512, att_size, bias=False)
self.fc1_3b = nn.Linear(att_size, 1, bias=True)
self.fc2_1a = nn.Linear(input_size, 768, bias=True)
self.fc2_1b = nn.Linear(768, 512, bias=True)
self.fc2_2a = nn.Linear(input_size, 768, bias=False)
self.fc2_2b = nn.Linear(768, 512, bias=False)
self.fc2_3a = nn.Linear(512, att_size, bias=False)
self.fc2_3b = nn.Linear(att_size, 1, bias=True)
self.fc = nn.Linear(input_size, output_size, bias=True)
def forward(self, ques_feat, img_feat): # ques_feat -- [batch, d] | img_feat -- [batch_size, m, d]
# print(img_feat.size(), ques_feat.size())
B = ques_feat.size(0)
ques_emb_1 = self.tan(self.dp(self.fc1_1a(ques_feat)))
ques_emb_1 = self.tan(self.dp(self.fc1_1b(ques_emb_1)))
img_emb_1 = self.tan(self.dp(self.fc1_2a(img_feat)))
img_emb_1 = self.tan(self.dp(self.fc1_2b(img_emb_1)))
#print(img_emb_1.shape,ques_emb_1.shape)
t1 = self.fc1_3a(ques_emb_1)
#print(t1.shape,img_emb_1.shape)
#pdb.set_trace()
h1 = self.tan(t1.view(-1,1,self.att_size) + img_emb_1)
h1_emb = self.tan(self.dp(self.fc1_3b(h1)))
p1 = self.sf(h1_emb.view(-1, self.img_seq_size)).view(B, 1, self.img_seq_size)
img_att1 = p1.matmul(img_feat)
u1 = ques_feat + img_att1.view(-1, self.input_size)
#(Question embedding aware with image features)
ques_emb_2 = self.tan(self.dp(self.fc2_1a(u1))) # [batch_size, att_size]
ques_emb_2 = self.tan(self.dp(self.fc2_1b(ques_emb_2)))
img_emb_2 = self.tan(self.dp(self.fc2_2a(img_feat)))
img_emb_2 = self.tan(self.dp(self.fc2_2b(img_emb_2)))
h2 = self.tan(self.fc2_3a(ques_emb_2).view(-1,1,self.att_size) + img_emb_2)
h2_emb = self.tan(self.dp(self.fc2_3b(h2)))
p2 = self.sf(h2_emb.view(-1, self.img_seq_size)).view(B, 1, self.img_seq_size)
img_att2 = p2.matmul(img_feat)
u2 = u1 + img_att2.view(-1, self.input_size)
score = self.fc(u2)
return score
class VqaModel(nn.Module):
def __init__(self, embed_size, qst_vocab_size, ans_vocab_size, word_embed_size, num_layers, hidden_size,att_size,img_seq_size):
super(VqaModel, self).__init__()
self.img_encoder = ImgEncoder(embed_size)
self.qst_encoder = QstEncoder(qst_vocab_size, word_embed_size, embed_size, num_layers, hidden_size)
self.att = Attention(embed_size,att_size,img_seq_size,ans_vocab_size, 0.5)
#self.tanh = nn.Tanh()
#self.dropout = nn.Dropout(0.5)
#self.fc1 = nn.Linear(embed_size, ans_vocab_size)
#self.fc2 = nn.Linear(ans_vocab_size, ans_vocab_size)
def forward(self, img, qst):
img_feature = self.img_encoder(img) # [batch_size, embed_size]
qst_feature = self.qst_encoder(qst) # [batch_size, embed_size]
#print(img_feature.shape,qst_feature.shape)
#pdb.set_trace()
output = self.att(qst_feature,img_feature) # [batch_size, ans_vocab_size=1000]
#combined_feature = torch.mul(img_feature, qst_feature) # [batch_size, embed_size]
#combined_feature = self.tanh(combined_feature)
#combined_feature = self.dropout(combined_feature)
#combined_feature = self.fc1(combined_feature) # [batch_size, ans_vocab_size=1000]
#combined_feature = self.tanh(combined_feature)
#combined_feature = self.dropout(combined_feature)
#combined_feature = self.fc2(combined_feature) # [batch_size, ans_vocab_size=1000]
return output