def getTfIdfWeights(params):
if params.get('tfIdf_file', '') == '':
dataset = json.load(
open(
'/triton/ics/project/imagedb/picsom/databases/COCO/download/annotations/captions_train2014.json',
'r'))
tokenizer = PTBTokenizer()
origRefs = {}
curr_keys = set()
n = params.get('max_ngram', 4)
for anns in dataset['annotations']:
if anns['image_id'] not in curr_keys:
origRefs[anns['image_id']] = []
curr_keys.add(anns['image_id'])
origRefs[anns['image_id']].append(anns)
origRefs = tokenizer.tokenize(origRefs)
doc_freq = Counter()
for refs in origRefs.iteritems():
rCounter = Counter()
for s in refs[1]:
rCounter += precook(s, n)
for ngrams in rCounter.keys():
doc_freq[ngrams] += 1
tfidf = {'doc_freq': doc_freq, 'N': len(origRefs)}
pickle.dump(tfidf, open('tf_idf_ngrams_4_allTrnRefs.p', 'w'))
else:
tfidf = pickle.load(open(params.get('tfIdf_file'), 'r'))
return tfidf
def getTfIdfWeights(params):
if params.get('tfIdf_file','') == '':
dataset = json.load(open('/triton/ics/project/imagedb/picsom/databases/COCO/download/annotations/captions_train2014.json','r'))
tokenizer = PTBTokenizer()
origRefs = {}
curr_keys = set()
n = params.get('max_ngram',4)
for anns in dataset['annotations']:
if anns['image_id'] not in curr_keys:
origRefs[anns['image_id']] = []
curr_keys.add(anns['image_id'])
origRefs[anns['image_id']].append(anns)
origRefs = tokenizer.tokenize(origRefs)
doc_freq = Counter()
for refs in origRefs.iteritems():
rCounter = Counter()
for s in refs[1]:
rCounter += precook(s,n)
for ngrams in rCounter.keys():
doc_freq[ngrams] += 1
tfidf = {'doc_freq':doc_freq,'N':len(origRefs)}
pickle.dump(tfidf,open('tf_idf_ngrams_4_allTrnRefs.p','w'))
else:
tfidf = pickle.load(open(params.get('tfIdf_file'),'r'))
return tfidf
def eval_prep_refs(split, dp, eval_metric):
refsById = defaultdict(list)
for s in dp.iterSentences(split=split):
refsById[s['cocoid']].append({
'image_id': s['cocoid'],
'id': s['sentid'],
'caption': s['raw']
})
tokenizer = PTBTokenizer()
refsById = tokenizer.tokenize(refsById)
if type(eval_metric) != list:
eval_metric = [eval_metric]
scorer = []
scorer_name = []
for evm in eval_metric:
if evm == 'meteor':
scorer.append(Meteor())
scorer_name.append("METEOR")
elif evm == 'spice':
scorer.append(Spice())
scorer_name.append("Spice")
elif evm == 'cider':
scorer.append(Cider())
scorer_name.append("CIDEr")
elif evm == 'rouge':
scorer.append(Rouge())
scorer_name.append("ROUGE_L")
elif evm[:4] == 'bleu':
bn = int(evm.split('_')[1])
scorer.append(Bleu(bn))
scorer_name_temp = ["Bleu_1", "Bleu_2", "Bleu_3", "Bleu_4"]
scorer_name.append(scorer_name_temp[:bn])
elif evm == 'len':
scorer.append(lenComputer())
scorer_name.append("Mean_Len")
elif evm[:3] == 'div':
dn = int(evm.split('_')[1])
scorer.append(divComputer(dn))
scorer_name.append("Global_Div_" + str(dn))
elif evm[:6] == 'lcldiv':
dn = int(evm.split('_')[1])
scorer.append(lcldivComputer(dn))
scorer_name.append("Local_Div_" + str(dn))
else:
raise ValueError('ERROR: %s --> Unsupported eval metric' % (evm))
return refsById, {
'scr_fn': scorer,
'scr_name': scorer_name,
'tokenizer': tokenizer
}
def main(params):
tokenizer = PTBTokenizer()
for resF in params['resFileList']:
caps = json.load(open(resF, 'r'))
capsById = {}
idTocaps = {}
n_cands = params['keepN'] - 1 if params['keepN'] != None else None
n = 0
for i, img in enumerate(caps['imgblobs']):
imgid = int(img['img_path'].split('_')[-1].split('.')[0])
capsById[imgid] = [{
'image_id': imgid,
'caption': img['candidate']['text'],
'id': n
}]
idTocaps[imgid] = i
n += 1
capsById[imgid].extend([{
'image_id': imgid,
'caption': cd['text'],
'id': n + j
} for j, cd in enumerate(img['candidatelist'][:n_cands])])
if len(capsById[imgid]) < (n_cands + 1):
capsById[imgid].extend([
capsById[imgid][-1]
for _ in xrange(n_cands + 1 - len(capsById[imgid]))
])
n += len(capsById[imgid]) - 1
n_caps_perimg = len(capsById[capsById.keys()[0]])
print n_caps_perimg
capsById = tokenizer.tokenize(capsById)
div_1, adiv_1 = compute_div_n(capsById, 1)
div_2, adiv_2 = compute_div_n(capsById, 2)
globdiv_1, _ = compute_global_div_n(capsById, 1)
print 'Diversity Statistics are as follows: \n Div1: %.2f, Div2: %.2f, gDiv1: %d\n' % (
div_1, div_2, globdiv_1)
if params['compute_mbleu']:
scorer = Bleu(4)
# Run 1 vs rest bleu metrics
all_scrs = []
scrperimg = np.zeros((n_caps_perimg, len(capsById)))
for i in xrange(n_caps_perimg):
tempRefsById = {}
candsById = {}
for k in capsById:
tempRefsById[k] = capsById[k][:i] + capsById[k][i + 1:]
candsById[k] = [capsById[k][i]]
score, scores = scorer.compute_score(tempRefsById, candsById)
all_scrs.append(score)
scrperimg[i, :] = scores[1]
all_scrs = np.array(all_scrs)
if params['writeback']:
for i, imgid in enumerate(capsById.keys()):
caps['imgblobs'][
idTocaps[imgid]]['mBleu_2'] = scrperimg[:, i].mean()
caps['imgblobs'][
idTocaps[imgid]]['candidate']['mBleu_2'] = scrperimg[0,
i]
for j, st in enumerate(caps['imgblobs'][idTocaps[imgid]]
['candidatelist'][:n_cands]):
caps['imgblobs'][idTocaps[imgid]]['candidatelist'][j][
'mBleu_2'] = scrperimg[1 + j, i]
json.dump(caps, open(resF, 'w'))
print 'Mean mutual Bleu scores on this set is:\nmBLeu_1, mBLeu_2, mBLeu_3, mBLeu_4\n'
print all_scrs.mean(axis=0)
def main(params):
resInp = json.load(open(params['inputCands'],'r'))
resGtImgid = defaultdict(list)
resCandsImgid = defaultdict(list)
icnt = 0
for i,doc in enumerate(resInp['docs']):
imgid = str(i)
for j,st in enumerate(doc['sents']):
if type(st)==list:
for sent in st:
resCandsImgid[imgid+'+'+str(j)].append({'image_id':imgid,'caption':' '.join(sent['trans'].split()[:-1]),'id':icnt})
resGtImgid[imgid+'+'+str(j)].append({'image_id':imgid,'caption':' '.join(sent['sent'].split()[:-1]),'id':icnt})
icnt+=1
else:
resCandsImgid[imgid+'+'+str(j)].append({'image_id':imgid,'caption':' '.join(st['trans'].split()[:-1]),'id':icnt})
resGtImgid[imgid+'+'+str(j)].append({'image_id':imgid,'caption':' '.join(st['sent'].split()[:-1]),'id':icnt})
icnt+=1
tokenizer = PTBTokenizer()
resCandsImgid = tokenizer.tokenize(resCandsImgid)
resGtImgid = tokenizer.tokenize(resGtImgid)
eval_metric = params['eval_metric']
if eval_metric == 'meteor':
scorer = Meteor()
scorer_name = "METEOR"
elif eval_metric == 'cider':
scorer = Cider()
scorer_name = "CIDEr"
elif eval_metric == 'rouge':
scorer = Rouge()
scorer_name = "ROUGE_L"
elif eval_metric[:4] == 'bleu':
bn = int(eval_metric.split('_')[1])
scorer = Bleu(bn)
scorer_name = ["Bleu_1", "Bleu_2", "Bleu_3", "Bleu_4"]
scorer_name = scorer_name[:bn]
else:
raise ValueError('ERROR: %s --> Unsupported eval metric'%(eval_metric))
lenDict = defaultdict(list)
for k in resCandsImgid:
lenDict[len(resCandsImgid[k])].append(k)
maxlen = max(lenDict.keys())
print 'Max length: %d'%maxlen
for i in xrange(maxlen):
res ={}
gts = {}
for k in resGtImgid.keys():
if i < len(resCandsImgid[k]):
res[k] = [resCandsImgid[k][i]]
gts[k] = resGtImgid[k]
print 'Now in %d, Lengths %d'%(i, len(gts))
t0 = time.time()
score, scores = scorer.compute_score(gts, res)
dt = time.time() - t0
print 'Done %d in %.3fs, score = %.3f' %(i, dt, score)
icnt = 0
for si,k in enumerate(gts.keys()):
idx,sidx = map(int,k.split('+'))
if type(st)==list:
resInp['docs'][idx]['sents'][sidx][i][scorer_name] = scores[si]
else:
resInp['docs'][idx]['sents'][sidx][scorer_name] = scores[si]
assert(len(scores) == si+1)
#pickle.dump(candScoresImgid,open('candScrMeteor_4AuxCmmePgoogSwapPposJJ_fullVal.json','w'))
json.dump(resInp,open(params['inputCands'],'w'))
import cPickle as pickle
dataset = json.load(open('/triton/ics/project/imagedb/picsom/databases/COCO/download/annotations/captions_val2014.json','r'))
resMulti = json.load(open('example_images/result_struct_4AuxCmmePgoogSwapPposJJ_fullVal.json','r'))
resAllImgid = defaultdict(list)
for img in dataset['annotations']:
resAllImgid[img['image_id']].append(img)
resCandsImgid = defaultdict(list)
icnt = 0
for img in resMulti['imgblobs']:
imgid = int(img['img_path'].split('_')[-1].split('.')[0])
for s in img['candidatelist']:
resCandsImgid[imgid].append({'image_id':imgid,'caption':s['text'],'id':icnt})
icnt+=1
from eval.mseval.pycocoevalcap.meteor.meteor import Meteor
from eval.mseval.pycocoevalcap.tokenizer.ptbtokenizer import PTBTokenizer
tokenizer = PTBTokenizer()
resCandsImgid = tokenizer.tokenize(resCandsImgid)
resAllImgid = tokenizer.tokenize(resAllImgid)
scorer = Meteor()
lenDict = defaultdict(list)
for k in resCandsImgid:
lenDict[len(resCandsImgid[k])].append(k)
maxlen = max(lenDict.keys())
print maxlen
candScoresImgid = defaultdict(list)
for i in xrange(maxlen):
res ={}
gts = {}
def main(params):
tokenizer = PTBTokenizer()
scorer = Spice(multihyp=1)
refsJs = json.load(open(params['refdata'], 'r'))
refsById = defaultdict(list)
for i, ann in enumerate(refsJs['annotations']):
refsById[ann['image_id']].append({
'image_id': ann['image_id'],
'id': i,
'caption': ann['caption']
})
refsById = tokenizer.tokenize(refsById)
n_cands = params['keepN'] - 1 if params['keepN'] != None else None
for resF in params['resFileList']:
caps = json.load(open(resF, 'r'))
capsById = {}
n = 0
n_cands_per_img = np.zeros((len(caps['imgblobs'])), dtype=np.int32)
for i, img in enumerate(caps['imgblobs']):
imgid = int(img['img_path'].split('_')[-1].split('.')[0])
capsById[imgid] = [{
'image_id': imgid,
'caption': img['candidate']['text'],
'id': n
}]
n += 1
capsById[imgid].extend([{
'image_id': imgid,
'caption': cd['text'],
'id': n + j
} for j, cd in enumerate(img['candidatelist'][:n_cands])])
n += len(capsById[imgid]) - 1
n_cands_per_img[i] = len(capsById[imgid])
capsById = tokenizer.tokenize(capsById)
print 'Maximum number of candidates is %d, mean is %.2f' % (
n_cands_per_img.max(), n_cands_per_img.mean())
refToks = {
imgid: refsById[imgid]
for imgid in capsById if imgid in refsById
}
if len(refToks) < len(capsById):
capsById = {imgid: capsById[imgid] for imgid in refToks}
n_refs_perimg = len(refToks[refToks.keys()[0]])
all_scrs = []
#met = [[] for i in xrange(len(eval_metric)) if eval_metric[i][:6] != 'lcldiv']
if params['rev_eval'] == 1:
tempCont = capsById
capsById = refToks
refToks = tempCont
if params['iterativeEval']:
npfilename = osp.join(
'scorelogs',
osp.basename(resF).split('.')[0] + '_iterativeSpice_%d' %
(params['keepN']))
if params[
'refdata'] != '/BS/databases/coco/annotations/captions_val2014.json':
npfilename += '_visgenome'
if params['singleHyp']:
npfilename += '_singlehyp'
iterIdces = np.arange(n_cands_per_img.max(), dtype=np.int32)
else:
iterIdces = [n_cands_per_img.max() - 1]
mean_scr = np.zeros((len(iterIdces)))
prec = np.zeros((len(iterIdces), len(capsById), 7))
rec = np.zeros((len(iterIdces), len(capsById), 7))
f_scr = np.zeros((len(iterIdces), len(capsById), 7))
for ii, idx in enumerate(iterIdces):
candsInp = {
imgid: [capsById[imgid][min(idx,
len(capsById[imgid]) - 1)]]
for imgid in capsById
} if params['singleHyp'] else {
imgid: capsById[imgid][:idx + 1]
for imgid in capsById
}
mean_scr[ii], all_scores = scorer.compute_score(refToks, candsInp)
#Compute mean precision and recalls
categories = all_scores[0].keys()
for i, scr in enumerate(all_scores):
for j, cat in enumerate(categories):
f_scr[ii, i, j] = scr[cat]['f']
prec[ii, i, j] = scr[cat]['pr']
rec[ii, i, j] = scr[cat]['re']
print 'At idx %d, prec = %.3f, rec= %.3f' % (
idx, prec[ii, :, 0].mean(), rec[ii, :, 0].mean())
if params['iterativeEval']:
np.savez(npfilename + '.npz',
mean_scr=mean_scr,
prec=prec,
rec=rec,
keys=refToks.keys())
prec = np.nan_to_num(prec)
rec = np.nan_to_num(rec)
print '---------------------\nmean spice is %.3f\n---------------------\n Per category scores are' % (
mean_scr[-1])
for j, cat in enumerate(categories):
print '%12s: f = %.3f, prec = %.3f, rec= %.3f' % (
cat, np.nan_to_num(f_scr[-1, :, j]).mean(), prec[-1, :,
j].mean(),
rec[-1, :, j].mean()) #~np.isnan(f_scr[-1,:,j])