-
Notifications
You must be signed in to change notification settings - Fork 78
/
model_cutie_res_bert.py
72 lines (62 loc) · 3.27 KB
/
model_cutie_res_bert.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
# written by Xiaohui Zhao
# 2018-12
import tensorflow as tf
from model_cutie import CUTIE
class CUTIERes(CUTIE):
def __init__(self, num_vocabs, num_classes, params, trainable=True):
self.name = "CUTIE_residual_bert_8x" # 8x down sampling
self.data = tf.placeholder(tf.int32, shape=[None, None, None, 1], name='grid_table')
self.gt_classes = tf.placeholder(tf.int32, shape=[None, None, None], name='gt_classes')
self.use_ghm = tf.equal(1, params.use_ghm) if hasattr(params, 'use_ghm') else tf.equal(1, 0) #params.use_ghm
self.activation = 'sigmoid' if (hasattr(params, 'use_ghm') and params.use_ghm) else 'relu'
self.ghm_weights = tf.placeholder(tf.float32, shape=[None, None, None, num_classes], name='ghm_weights')
self.layers = dict({'data': self.data, 'gt_classes': self.gt_classes, 'ghm_weights':self.ghm_weights})
self.num_vocabs = num_vocabs
self.num_classes = num_classes
self.trainable = trainable
self.embedding_size = params.embedding_size
self.weight_decay = params.weight_decay if hasattr(params, 'weight_decay') else 0.0
self.hard_negative_ratio = params.hard_negative_ratio if hasattr(params, 'hard_negative_ratio') else 0.0
self.batch_size = params.batch_size if hasattr(params, 'batch_size') else 0
self.layer_inputs = []
self.embedding_table = None
self.setup()
def setup(self):
# input
(self.feed('data')
.bert_embed(self.num_vocabs, 768, name='embeddings', trainable=False))
# encoder
(self.feed('embeddings')
.conv(3, 5, 64, 1, 1, name='encoder1_1')
.conv(3, 5, 128, 1, 1, name='encoder1_2')
.max_pool(2, 2, 2, 2, name='pool1')
.conv(3, 5, 128, 1, 1, name='encoder2_1')
.conv(3, 5, 256, 1, 1, name='encoder2_2')
.max_pool(2, 2, 2, 2, name='pool2')
.conv(3, 5, 256, 1, 1, name='encoder3_1')
.conv(3, 5, 512, 1, 1, name='encoder3_2')
.max_pool(2, 2, 2, 2, name='pool3')
.conv(3, 5, 512, 1, 1, name='encoder4_1')
.conv(3, 5, 512, 1, 1, name='encoder4_2'))
# decoder
(self.feed('encoder4_2')
.up_conv(3, 5, 512, 1, 1, name='up1'))
(self.feed('up1', 'encoder3_2')
.concat(3, name='concat1')
.conv(3, 5, 256, 1, 1, name='decoder1_1')
.conv(3, 5, 256, 1, 1, name='decoder1_2')
.up_conv(3, 5, 256, 1, 1, name='up2'))
(self.feed('up2', 'encoder2_2')
.concat(3, name='concat2')
.conv(3, 5, 128, 1, 1, name='decoder2_1')
.conv(3, 5, 128, 1, 1, name='decoder2_2')
.up_conv(3, 5, 128, 1, 1, name='up3'))
(self.feed('up3', 'encoder1_2')
.concat(3, name='concat3')
.conv(3, 5, 64, 1, 1, name='decoder3_1')
.conv(3, 5, 64, 1, 1, name='decoder3_2'))
# classification
(self.feed('decoder3_2')
.conv(1, 1, self.num_classes, 1, 1, activation=self.activation, name='cls_logits') # sigmoid for ghm
.softmax(name='softmax'))