From ade442ce78d2660d39fb69e8d59bc551b3da3dbf Mon Sep 17 00:00:00 2001
From: tgupta6 <tgupta6@illinois.edu>
Date: Wed, 19 Oct 2016 10:05:49 -0700
Subject: [PATCH] answer inference first transforms then bn then add different
feat
---
.../inference.py | 57 +++++++++++++++----
1 file changed, 47 insertions(+), 10 deletions(-)
diff --git a/answer_classifier_cached_features/inference.py b/answer_classifier_cached_features/inference.py
index 7f5d0c8..02da937 100644
--- a/answer_classifier_cached_features/inference.py
+++ b/answer_classifier_cached_features/inference.py
@@ -59,6 +59,8 @@ class AnswerInference():
# self.per_region_answer_scores = [None]*self.batch_size
obj_atr_qa_feat = [None]*self.batch_size
+ q_a_feat_list = [None]*self.batch_size
+ obj_atr_det_list = [None]*self.batch_size
for j in xrange(self.batch_size):
if j==0:
reuse_vars = False
@@ -112,24 +114,45 @@ class AnswerInference():
yes_no_feat_,
[self.num_regions, 1, 1])
- obj_atr_qa_feat[j] = tf.concat(
+
+ obj_atr_det_list[j] = tf.concat(
+ 2,
+ [self.selected_noun_adjective[j], obj_det_feat, atr_det_feat])
+
+ q_a_feat_list[j] = tf.concat(
2,
- [self.selected_noun_adjective[j], yes_no_feat_, obj_det_feat, atr_det_feat, q_feat, a_feat])
+ [yes_no_feat_, q_feat, a_feat])
# obj_atr_qa_feat[j] = tf.expand_dims(
# obj_atr_qa_feat[j],
# 0)
- obj_atr_qa_feat = tf.pack(obj_atr_qa_feat)
- print obj_atr_qa_feat.get_shape()
+ self.obj_atr_det_packed = tf.pack(obj_atr_det_list)
+ self.q_a_feat_packed = tf.pack(q_a_feat_list)
- self.per_region_answer_scores = layers.conv2d(
- obj_atr_qa_feat,
- 1,
+ self.obj_atr_det_conv_bn = self.conv_bn(
+ self.obj_atr_det_packed,
2500,
- 'per_region_ans_score_conv_1',
- func = None)
+ 'obj_atr_det_conv_bn')
+
+ self.q_a_feat_conv_bn = self.conv_bn(
+ self.q_a_feat_packed,
+ 2500,
+ 'q_a_feat_conv_bn')
+
+ self.obj_atr_qa_feat = tf.nn.relu(
+ self.obj_atr_det_conv_bn + self.q_a_feat_conv_bn)
+
+ #obj_atr_qa_feat = tf.pack(obj_atr_qa_feat)
+ #print obj_atr_qa_feat.get_shape()
+
+ # self.per_region_answer_scores = layers.conv2d(
+ # obj_atr_qa_feat,
+ # 1,
+ # 2500,
+ # 'per_region_ans_score_conv_1',
+ # func = None)
self.per_region_answer_scores = tf.nn.relu(
layers.batch_norm(
@@ -197,7 +220,21 @@ class AnswerInference():
feats = tf.transpose(tf.pack(feats), [1,0,2])
return feats
-
+
+ def conv_bn(feat,out_dim,name):
+ conv_feat = layers.conv2d(
+ feat,
+ 1,
+ out_dim,
+ name,
+ func = None)
+
+ bn_conv_feat = layers.batch_norm(
+ conv_feat,
+ tf.constant(self.is_training))
+
+ return bn_conv_feat
+
# def elementwise_product(self, obj_feat, atr_feat, ques_feat, ans_feat):
# tiled_ques = tf.tile(tf.reshape(ques_feat,[1, -1]),[self.num_answers,1])
# qa_feat = tf.concat(
--
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