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How to use addCap method in fMBT
Best Python code snippet using fMBT_python
json_pandas.py
Source:json_pandas.py 
1"""2json ë¶ë¬ìì 캡ì
ë¶ì´ë ê²3"""4import json5import pandas as pd6path = './datasets/vqa/v2_OpenEnded_mscoco_train2014_questions.json'7with open(path) as question:8 question = json.load(question)9# question['questions'][0]10# question['questions'][1]11# question['questions'][2]12df = pd.DataFrame(question['questions'])13df14caption_path = './datasets/caption/vis_st_trainval.json'15with open(caption_path) as cap:16 cap = json.load(cap)17df_cap = pd.DataFrame(cap)18df_cap19df_addcap = pd.merge(df, df_cap, how='left', on='image_id')20del df_addcap['file_path']21########################################################################################################################22"""23pandas to json24"""25df_addcap.to_json('./datasets/caption/train_cap2.json', orient='table')26with open('./datasets/caption/train_cap2.json') as train_cap:27 train_cap = json.load(train_cap)28########################################################################################################################29########################################################################################################################30"""31answer + cap32"""33path = '/home/nextgen/Desktop/mcan-vqa/datasets/vqa/v2_mscoco_train2014_annotations.json'34path = './datasets/vqa/v2_mscoco_val2014_annotations.json'35with open(path) as answer:36 answer = json.load(answer)37answer['annotations'][0]38df_ans = pd.DataFrame(answer['annotations'])39df_ans[:0]40del df_ans['question_type']41del df_ans['answers']42del df_ans['answer_type']43del df_ans['image_id']44df_ans[df_ans['question_id']==458752000]45df_addcap2 = pd.merge(df_addcap, df_ans, how='left', on='question_id')46df_addcap2[:0]47df_addcap2['multiple_choice_answer']48# del df_addcap['file_path']49df_addcap2.to_json('./datasets/caption/val_qacap.json', orient='table')50with open('./datasets/caption/train_qacap.json') as train_qacap:51 train_qacap = json.load(train_qacap)52########################################################################################################################53"""val testë ë§ì°¬ê°ì§"""54path = './datasets/vqa/v2_OpenEnded_mscoco_val2014_questions.json'55with open(path) as question:56 question = json.load(question)57df = pd.DataFrame(question['questions'])58df59caption_path = './datasets/caption/vis_st_trainval.json'60with open(caption_path) as cap:61 cap = json.load(cap)62df_cap = pd.DataFrame(cap)63df_cap64df_addcap = pd.merge(df, df_cap, how='left', on='image_id')65df_addcap[:0]66del df_addcap['file_path']67df_addcap.to_json('./datasets/caption/val_cap.json', orient='table')68#test69path = './datasets/vqa/v2_OpenEnded_mscoco_test-dev2015_questions.json'70with open(path) as question:71 question = json.load(question)72df = pd.DataFrame(question['questions'])73df74df['image_id'] = df.image_id.astype(int)75caption_path = './datasets/caption/vis_st_test.json'76with open(caption_path) as cap:77 cap = json.load(cap)78df_cap = pd.DataFrame(cap)79df_cap80df_cap['image_id'] = df_cap.image_id.astype(int)81df_addcap = pd.merge(df, df_cap, how='left', on='image_id')82df_addcap[:0]83del df_addcap['file_path']84df_addcap.to_json('./datasets/caption/test_cap.json', orient='table')85########################################################################################################################86from core.data.ans_punct import prep_ans87import numpy as np88import en_vectors_web_lg, random, re, json89import json90from core.data.data_utils import ques_load91stat_ques_list = \92 json.load(open('./datasets/caption/train_cap.json', 'r'))['data'] + \93 json.load(open('./datasets/caption/val_cap.json', 'r'))['data'] + \94 json.load(open('./datasets/caption/test_cap.json', 'r'))['data']95def tokenize(stat_ques_list, use_glove):96 token_to_ix = {97 'PAD': 0,98 'UNK': 1,99 }100 spacy_tool = None101 pretrained_emb = []102 if use_glove:103 spacy_tool = en_vectors_web_lg.load()104 pretrained_emb.append(spacy_tool('PAD').vector)105 pretrained_emb.append(spacy_tool('UNK').vector)106 for ques in stat_ques_list:107 words = re.sub(108 r"([.,'!?\"()*#:;])",109 '',110 ques['question'].lower()111 ).replace('-', ' ').replace('/', ' ').split()112 for word in words:113 if word not in token_to_ix:114 token_to_ix[word] = len(token_to_ix)115 if use_glove:116 pretrained_emb.append(spacy_tool(word).vector)117 for ques in stat_ques_list:118 words = re.sub(119 r"([.,'!?\"()*#:;])",120 '',121 ques['caption'].lower()122 ).replace('-', ' ').replace('/', ' ').split()123 for word in words:124 if word not in token_to_ix:125 token_to_ix[word] = len(token_to_ix)126 if use_glove:127 pretrained_emb.append(spacy_tool(word).vector)128 pretrained_emb = np.array(pretrained_emb)129 return token_to_ix, pretrained_emb130token_to_ix, pretrained_emb = tokenize(stat_ques_list, True)131#######################################################################################################################132# with open('./datasets/vqa/v2_mscoco_train2014_annotations.json') as answer:133# answer = json.load(answer)134#135# answer['annotations'][2]136"""137ëµì ì´ì©íëê±°ë¡ íë©´ train val ë¹êµë¡í´ì¼ í¨138testì
ì ëµì ì ê³µíì§ ììì testí ë ëµì ì´ì©íë 모ë¸ì ì¬ì©í ì ìì139"""140####141import cal_sim142import pandas as pd143with open('datasets/caption/train_cap.json') as train_cap:144 train_cap = json.load(train_cap)145with open('datasets/caption/val_cap.json') as val_cap:146 val_cap = json.load(val_cap)147with open('datasets/caption/test_cap.json') as test_cap:148 test_cap = json.load(test_cap)149df_train = pd.DataFrame(train_cap['data'])150df_val = pd.DataFrame(val_cap['data'])151df_test = pd.DataFrame(test_cap['data'])152df_train[:0]153# df_train['similarity'] = cal_sim.sent_sim((df_train['question'], dtype=int32), (df_train['caption'], dtype=int32))154df_train.iloc[0]['question']155def txt2vec(sentence):156 # s = sentence.split()157 tt = []158 new_i = re.sub(159 r"([.,'!?\"()*#:;])",160 '',161 sentence.lower()162 ).replace('-', ' ').replace('/', ' ').split()163 for i in new_i:164 num = token_to_ix[i]165 tt.append(pretrained_emb[num])166 return tt167stat_ques_list[0]168token_to_ix['what']169len(txt2vec(df_train.iloc[0]['question']))170df_train.iloc[0]['question']171df_train.iloc[0]['caption']172len(txt2vec(df_train.iloc[0]['caption']))173from numpy import dot174from numpy.linalg import norm175import numpy as np176def cos_sim(A, B):177 return dot(A, np.transpose(B)) / (norm(A) * norm(B))178def word_sim(w1,w2): #word simiarity179 s = 0.5 * (1+ cos_sim(w1,w2))180 return s181def sent_sim(ss1, ss2): #sentence simiarity182 s1 = txt2vec(ss1)183 s2 = txt2vec(ss2)184 t = []185 for i in s1[2:]: #question 0,1 are PAD, UNK186 tmp = []187 for j in s2[2:]: #caption188 tmp_sim = word_sim(i,j)189 tmp.append(tmp_sim)190 t.append(max(tmp))191 sentence_sim = sum(t) / len(s1[2:])192 return sentence_sim193t = sent_sim('yes', 'hello')194tmp = sent_sim(df_train.iloc[105]['question'], df_train.iloc[103]['caption'])195t1 = sent_sim('Is there a travel guide on the table?', 'A place of cake and coffee are on an outdoor table')196t2 = sent_sim('yes', 'A place of cake and coffee are on an outdoor table')197t3 = sent_sim('no', 'no')198df_train.iloc[105]['question'] #ì ì¬ë ì¢ ì´ìí ë¯ ë무 ëê² ëì¤ë ê² ê°ìëë199df_train.iloc[103]['caption']200cos_sim(txt2vec('e'), txt2vec('z'))201new_i = re.sub(202 r"([.,'!?\"()*#:;])",203 '',204 df_train.iloc[102]['question'].lower()205 ).replace('-', ' ').replace('/', ' ').split()...
questiongen.py
Source:questiongen.py
1from random import randint2from math import ceil,floor3from question_list import addition, subtraction, multiplication, division, mixed4class QuestionGenerator():5 def __init__(self, itemType, itemUnit, itemName, itemPluralName, itemCost, numberOfGuests, level, question_num):6 self.itemType = itemType7 self.itemName = itemName8 self.itemPlurName = itemPluralName9 self.itemUnit = int(itemUnit)10 self.itemCost = int(itemCost)11 self.guestsNum = int(numberOfGuests)12 self.level = int(level)13 self.question_num = int(question_num)14 if (self.level <= 3):15 self.multcap = 516 self.addcap = 2517 elif (self.level <= 6):18 self.multcap = 719 self.addcap = 5020 else:21 self.multcap = 922 self.addcap = 5023 self.divcap = self.multcap**224 self.subcap = self.addcap*225 def generate(self):26 add_q = addition (self.itemCost, self.itemUnit, self.itemPlurName, self.addcap, self.itemName)27 sub_q = subtraction (self.itemCost, self.itemUnit, self.itemPlurName, self.subcap, self.itemName)28 mult_q = multiplication (self.itemCost, self.itemUnit, self.itemPlurName, self.multcap, self.itemName)29 div_q = division (self.itemCost, self.itemUnit, self.itemPlurName, self.multcap, self.itemName, self.guestsNum)30 mix_q = mixed (self.itemCost, self.itemUnit, self.itemPlurName, self.multcap, self.itemName, self.guestsNum)31 if(self.question_num == 1):32 return addition(self.itemCost, self.itemUnit, self.itemPlurName, self.addcap, self.itemName)33 elif(self.question_num == 2):34 return subtraction(self.itemCost, self.itemUnit, self.itemPlurName, self.subcap, self.itemName)35 elif(self.question_num == 3):36 return multiplication(self.itemCost, self.itemUnit, self.itemPlurName, self.multcap, self.itemName)37 elif(self.question_num == 4):38 return division(self.itemCost, self.itemUnit, self.itemPlurName, self.multcap, self.itemName, self.guestsNum)39 elif(self.question_num == 5):...
addcap.py
Source:addcap.py
1#!/usr/bin/python2import sys,os3usage="""4Add Cap Using tLEaP.5Firstly addh6Then add cap NME and ACE to the C- and N- terminal of each peptide7Usage $0 in.pdb out.pdb8"""9if len(sys.argv)<3:10 print usage11 sys.exit()12infile,outfile=sys.argv[1:3]13leap1in='''14source leaprc.ff14SB15rec = loadpdb %s16savepdb rec addcap_tmp.pdb17quit18'''19ofp=open("addcap_tleap1.in","w")20ofp.write(leap1in%infile)21ofp.close()22os.system("tleap -f addcap_tleap1.in")23firstresi=True24lastter=True25cachedlines=""26oldindex=027ofp=open("addcap_tmp2.pdb","w")28for line in open("addcap_tmp.pdb","r"):29 if len(line)>30:index=int(line[22:26])30 if index!=oldindex:31 firstresi=lastter32 oldindex=index33 if firstresi:34 if " H1 " not in line and " H2 " not in line and " H3 " not in line :35 cachedlines=cachedlines+line36 if not firstresi:37 ofp.write(cachedlines)38 cachedlines=""39 if "OXT" in line:40 ofp.write("%s N NME%s"%(line[:12],line[20:]))41 elif "H3 " in line:42 ofp.write("%s C ACE%s"%(line[:12],line[20:]))43 elif not firstresi:44 ofp.write(line)45 else:46 pass47 lastter=("TER" in line)48ofp.close()49leap2in='''50source leaprc.ff14SB51rec = loadpdb addcap_tmp2.pdb52savepdb rec %s53quit54'''55ofp=open("addcap_leap2.in","w")56ofp.write(leap2in%outfile)57ofp.close()...
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