def calRandomAcc(misc):
utils.mlpOPlable(np.zeros(len(misc['bowAnswerTest'])),
misc['bowAnswerTest'], misc['answerGroup'],
misc['numAnswer'])
randintx = [randint(0, 499) for p in range(len(misc['bowAnswerTest']))]
utils.mlpOPlable(randintx, misc['bowAnswerTest'], misc['answerGroup'],
misc['numAnswer'])
def Prior_baseline(num_hidden=50,
K=500,
Type=0,
isBinary=0,
classifier='MLP',
CNNfeat='fc7',
L2=0.001,
C=1):
# Type, a list indicate which we want to use.
dataset = 'coco'
misc = {}
misc['C'] = C
misc['Type'] = Type
misc['IsBinary'] = isBinary
misc['numAnswer'] = K
misc['numHidden'] = num_hidden
misc['vali_size'] = 20000
misc['classifier'] = classifier
misc['CNNfeat'] = CNNfeat
misc['L2'] = L2
dp = getDataProvider(dataset, misc['IsBinary'])
print 'The K number is %d' % (misc['numAnswer'])
# dp.downloadImage()
# dp.loadCaption()
# get the vocabulary for the answers.
misc['Awordtoix'], misc['Aixtoword'], misc[
'Avocab'] = preProBuildAnswerVocab(dp.iterAnswer('train'),
misc['numAnswer'])
misc['bowAnswerTrain'] = BoWAnswerEncoding(dp.iterAnswer('train'),
misc['Awordtoix'])
misc['bowAnswerTest'] = BoWAnswerEncoding(dp.iterAnswer('test'),
misc['Awordtoix'])
misc['multiAnswerTest'] = BOWMultiAnswerEncoding(
dp.iterMultiAnswer('test'), misc['Awordtoix'])
misc['genCaptionTest'] = BOWMultiAnswerEncoding(dp.iterGenCaption('test'),
misc['Awordtoix'])
misc['answerGroup'] = FindAnswerGroup(dp.iterImgIdQuestion('test'))
for misc['th'] in range(150, 400, 25):
print 'th value is %d' % misc['th']
answer_counts, vocab, ques_depth = preProBuildAnswerVocabTop(
dp, misc['th'])
idx = 0
ans_counts = {}
for sent in dp.iterQuestion('train'):
string = ' '.join(sent[:ques_depth[idx]])
if string in vocab:
if ans_counts.get(string,
misc['numAnswer']) == misc['numAnswer']:
ans_counts[string] = [misc['bowAnswerTrain'][idx]]
else:
ans_counts[string] = ans_counts.get(
string,
misc['numAnswer']) + [misc['bowAnswerTrain'][idx]]
idx += 1
# get the prior of the label
ans_prior = {}
for key, value in ans_counts.iteritems():
tmp = {}
for idx in value:
tmp[idx] = tmp.get(idx, 0) + 1
tmp_idx = sorted(tmp, key=tmp.get, reverse=True)[0]
ans_prior[key] = misc['Aixtoword'].get(tmp_idx)
for i in range(6):
print "Depth %d" % (i + 1)
for key, value in sorted(ans_counts.iteritems(),
key=lambda (k, v): (v, k)):
if len(key.split(' ')) == i + 1:
print "%s: %s " % (key, len(value)),
print ""
for i in range(6):
print "Depth %d" % (i + 1)
for tmp in vocab:
if len(tmp.split(' ')) == i + 1:
print "%s: %s " % (tmp, ans_prior.get(tmp)),
print ""
depth_count = {}
for tmp in vocab:
count = len(tmp.split(' '))
depth_count[count] = depth_count.get(count, 0) + 1
pdb.set_trace()
max_depth = max(ques_depth)
ans = []
for sent in dp.iterQuestion('test'):
# iter from big to small
tmp = ''
for depth in range(max_depth):
string = ' '.join(sent[:ques_depth[max_depth - depth]])
if string in vocab:
tmp = string
break
if tmp != '':
idx = misc['Awordtoix'].get(ans_prior.get(string))
ans.append(idx)
else:
ans.append(0)
utils.mlpOPlable(ans, misc['bowAnswerTest'], misc['answerGroup'],
misc['numAnswer'])
openAnswerWriteJson(ans, dp.iterAll('test'), misc)
def Prior_baseline(num_hidden=50, K=500, Type=0, isBinary=0, classifier="MLP", CNNfeat="fc7", L2=0.001, C=1):
# Type, a list indicate which we want to use.
dataset = "coco"
misc = {}
misc["C"] = C
misc["Type"] = Type
misc["IsBinary"] = isBinary
misc["numAnswer"] = K
misc["numHidden"] = num_hidden
misc["vali_size"] = 20000
misc["classifier"] = classifier
misc["CNNfeat"] = CNNfeat
misc["L2"] = L2
dp = getDataProvider(dataset, misc["IsBinary"])
print "The K number is %d" % (misc["numAnswer"])
# dp.downloadImage()
# dp.loadCaption()
# get the vocabulary for the answers.
misc["Awordtoix"], misc["Aixtoword"], misc["Avocab"] = preProBuildAnswerVocab(
dp.iterAnswer("train"), misc["numAnswer"]
)
misc["bowAnswerTrain"] = BoWAnswerEncoding(dp.iterAnswer("train"), misc["Awordtoix"])
misc["bowAnswerTest"] = BoWAnswerEncoding(dp.iterAnswer("test"), misc["Awordtoix"])
misc["multiAnswerTest"] = BOWMultiAnswerEncoding(dp.iterMultiAnswer("test"), misc["Awordtoix"])
misc["genCaptionTest"] = BOWMultiAnswerEncoding(dp.iterGenCaption("test"), misc["Awordtoix"])
misc["answerGroup"] = FindAnswerGroup(dp.iterImgIdQuestion("test"))
for misc["th"] in range(150, 400, 25):
print "th value is %d" % misc["th"]
answer_counts, vocab, ques_depth = preProBuildAnswerVocabTop(dp, misc["th"])
idx = 0
ans_counts = {}
for sent in dp.iterQuestion("train"):
string = " ".join(sent[: ques_depth[idx]])
if string in vocab:
if ans_counts.get(string, misc["numAnswer"]) == misc["numAnswer"]:
ans_counts[string] = [misc["bowAnswerTrain"][idx]]
else:
ans_counts[string] = ans_counts.get(string, misc["numAnswer"]) + [misc["bowAnswerTrain"][idx]]
idx += 1
# get the prior of the label
ans_prior = {}
for key, value in ans_counts.iteritems():
tmp = {}
for idx in value:
tmp[idx] = tmp.get(idx, 0) + 1
tmp_idx = sorted(tmp, key=tmp.get, reverse=True)[0]
ans_prior[key] = misc["Aixtoword"].get(tmp_idx)
for i in range(6):
print "Depth %d" % (i + 1)
for key, value in sorted(ans_counts.iteritems(), key=lambda (k, v): (v, k)):
if len(key.split(" ")) == i + 1:
print "%s: %s " % (key, len(value)),
print ""
for i in range(6):
print "Depth %d" % (i + 1)
for tmp in vocab:
if len(tmp.split(" ")) == i + 1:
print "%s: %s " % (tmp, ans_prior.get(tmp)),
print ""
depth_count = {}
for tmp in vocab:
count = len(tmp.split(" "))
depth_count[count] = depth_count.get(count, 0) + 1
pdb.set_trace()
max_depth = max(ques_depth)
ans = []
for sent in dp.iterQuestion("test"):
# iter from big to small
tmp = ""
for depth in range(max_depth):
string = " ".join(sent[: ques_depth[max_depth - depth]])
if string in vocab:
tmp = string
break
if tmp != "":
idx = misc["Awordtoix"].get(ans_prior.get(string))
ans.append(idx)
else:
ans.append(0)
utils.mlpOPlable(ans, misc["bowAnswerTest"], misc["answerGroup"], misc["numAnswer"])
openAnswerWriteJson(ans, dp.iterAll("test"), misc)
def calRandomAcc(misc):
utils.mlpOPlable(
np.zeros(len(misc["bowAnswerTest"])), misc["bowAnswerTest"], misc["answerGroup"], misc["numAnswer"]
)
randintx = [randint(0, 499) for p in range(len(misc["bowAnswerTest"]))]
utils.mlpOPlable(randintx, misc["bowAnswerTest"], misc["answerGroup"], misc["numAnswer"])
def calRandomAcc(misc):
utils.mlpOPlable(np.zeros(len(misc['bowAnswerTest'])), misc[
'bowAnswerTest'], misc['answerGroup'], misc['numAnswer'])
randintx = [randint(0, 499) for p in range(len(misc['bowAnswerTest']))]
utils.mlpOPlable(randintx, misc['bowAnswerTest'], misc['answerGroup'], misc['numAnswer'])
def Prior_baseline(num_hidden=50, K = 500, Type = 0, isBinary=0, classifier = 'MLP', CNNfeat = 'fc7', L2 = 0.001, C = 1):
# Type, a list indicate which we want to use.
dataset = 'coco'
word_count_threshold = 1
misc= {}
misc['C'] = C
misc['Type'] = Type
misc['IsBinary'] = isBinary
misc['numAnswer'] = K
misc['numHidden'] = num_hidden
misc['vali_size'] = 20000
misc['classifier'] =classifier
misc['CNNfeat'] = CNNfeat
misc['L2'] = L2
L1 = 0
dp = getDataProvider(dataset, misc['IsBinary'])
print 'The K number is %d' %(misc['numAnswer'])
#dp.downloadImage()
#dp.loadCaption()
# get the vocabulary for the answers.
misc['Awordtoix'], misc['Aixtoword'], misc['Avocab'] = preProBuildAnswerVocab(dp.iterAnswer('train'),misc['numAnswer'])
misc['bowAnswerTrain'] = BoWAnswerEncoding(dp.iterAnswer('train'), misc['Awordtoix'])
misc['bowAnswerTest'] = BoWAnswerEncoding(dp.iterAnswer('test'), misc['Awordtoix'])
misc['multiAnswerTest'] = BOWMultiAnswerEncoding(dp.iterMultiAnswer('test'), misc['Awordtoix'])
misc['genCaptionTest'] = BOWMultiAnswerEncoding(dp.iterGenCaption('test'), misc['Awordtoix'])
misc['answerGroup'] = FindAnswerGroup(dp.iterImgIdQuestion('test'))
result = {}
for misc['th'] in range(150, 400, 25):
print 'th value is %d' %misc['th']
answer_counts, vocab, ques_depth = preProBuildAnswerVocabTop(dp, misc['th'])
idx = 0
ans_counts = {}
for sent in dp.iterQuestion('train'):
string = ' '.join(sent[:ques_depth[idx]])
if string in vocab:
if ans_counts.get(string, misc['numAnswer']) == misc['numAnswer']:
ans_counts[string] = [misc['bowAnswerTrain'][idx]]
else:
ans_counts[string] = ans_counts.get(string, misc['numAnswer']) + [misc['bowAnswerTrain'][idx]]
idx += 1
# get the prior of the label
ans_prior = {}
for key, value in ans_counts.iteritems():
tmp = {}
for idx in value:
tmp[idx] = tmp.get(idx, 0) + 1
tmp_idx = sorted(tmp, key=tmp.get, reverse=True)[0]
ans_prior[key] = misc['Aixtoword'].get(tmp_idx)
for i in range(6):
print "Depth %d" %(i+1)
for key, value in sorted(ans_counts.iteritems(), key=lambda (k,v): (v,k)):
if len(key.split(' '))==i+1:
print "%s: %s " % (key, len(value)),
print ""
for i in range(6):
print "Depth %d" %(i+1)
for tmp in vocab:
if len(tmp.split(' '))==i+1:
print "%s: %s " % (tmp, ans_prior.get(tmp)),
print ""
depth_count = {}
for tmp in vocab:
count = len(tmp.split(' '))
depth_count[count] = depth_count.get(count, 0) + 1
pdb.set_trace()
max_depth = max(ques_depth)
ans = []
for sent in dp.iterQuestion('test'):
# iter from big to small
tmp = ''
for depth in range(max_depth):
string = ' '.join(sent[:ques_depth[max_depth - depth]])
if string in vocab:
tmp = string
break
if tmp != '':
idx = misc['Awordtoix'].get(ans_prior.get(string))
ans.append(idx)
else:
ans.append(0)
utils.mlpOPlable(ans, misc['bowAnswerTest'], misc['answerGroup'], misc['numAnswer'])
openAnswerWriteJson(ans, dp.iterAll('test'), misc)
