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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 ìº¡ì 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¨138testìì ëµì ì ê³µíì§ ììì testí  ë ëµì ì´ì©íë ëª¨ë¸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¯ ëë¬´ 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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