def eval_captions(gt_captions, res_captions):
"""
gt_captions = ground truth captions; 5 per image
res_captions = captions generated by the model to be evaluated
"""
print('ground truth captions')
print(gt_captions)
print('RES CAPTIONS')
print(res_captions)
scorers = [
(Bleu(4), ["Bleu_1", "Bleu_2", "Bleu_3", "Bleu_4"]),
(Meteor(),"METEOR"),
(Rouge(), "ROUGE_L"),
(Cider(), "CIDEr"),
]
res = []
for scorer, method in scorers:
print('computing %s score...' % (scorer.method()))
score, scores = scorer.compute_score(gt_captions, res_captions)
if type(method) == list:
for sc, scs, m in zip(score, scores, method):
print("%s: %0.3f"%(m, sc))
res.append((m, sc))
else:
print("%s: %0.3f"%(method, score))
res.append((method, score))
return res
def test_score():
cand = "中华人民共和国"
ref = "中华人民共和国公民"
bleu = Bleu(N_SIZE)
bleu.add_inst(cand, ref)
s = bleu.get_score()
print('score: {}'.format(s))
def test_add_inst():
cand = '13'
ref = '13'
bleu = Bleu(N_SIZE)
bleu.add_inst(cand, ref)
match_ngram = bleu.match_ngram
candi_ngram = bleu.candi_ngram
print('match_ngram: {}'.format(match_ngram))
print('candi_ngram: {}'.format(candi_ngram))
def evaluate(self):
cap = open(r'results.txt')
cap_ = []
for line in cap:
line = line.split(' ')
line[len(line)-1] = '.'
del line[0]
print(line)
cap_.append(line)
gts = {}
res = {}
f = open("cap_flickr30k.json")
captions = json.load(f)
f1 = open("dic_flickr30k.json")
dics = json.load(f1)
dics = dics['images']
pos = 0
for i in range(0, len(dics), 1):
if dics[i]['split'] == 'test':
caption_1 = []
caption_2 = []
caption_1.append(captions[i][0]['caption'])
res[dics[i]['id']] = caption_1
caption_2.append(cap_[pos])
caption_2.append(cap_[pos])
gts[dics[i]['id']] = caption_2
pos = pos + 1
# =================================================
# Set up scorers
# =================================================
# =================================================
# Set up scorers
# =================================================
print('setting up scorers...')
scorers = [
(Bleu(4), ["Bleu_1", "Bleu_2", "Bleu_3", "Bleu_4"]),
(Cider(), "CIDEr")
]
# =================================================
# Compute scores
# =================================================
eval = {}
for scorer, method in scorers:
print ('computing %s score...'%(scorer.method()))
score, scores = scorer.compute_score(gts, res)
if type(method) == list:
for sc, scs, m in zip(score, scores, method):
self.setEval(sc, m)
print ("%s: %0.3f"%(m, sc))
else:
self.setEval(score, method)
print ("%s: %0.3f"%(method, score))
def compute_bleu_rouge(pred_dict, ref_dict, bleu_order=4):
"""
Compute bleu and rouge scores.
"""
assert set(pred_dict.keys()) == set(ref_dict.keys()), \
"missing keys: {}".format(
set(ref_dict.keys()) - set(pred_dict.keys()))
scores = {}
bleu_scores, _ = Bleu(bleu_order).compute_score(ref_dict, pred_dict)
for i, bleu_score in enumerate(bleu_scores):
bleu_score *= 100
scores['Bleu-%d' % (i + 1)] = bleu_score
return scores
def evaluate(self):
imgIds = self.params['image_id']
# imgIds = self.coco.getImgIds()
gts = {}
res = {}
for imgId in imgIds:
gts[imgId] = self.coco.imgToAnns[imgId]
res[imgId] = self.cocoRes.imgToAnns[imgId]
# =================================================
# Set up scorers
# =================================================
print('tokenization...')
tokenizer = PTBTokenizer()
gts = tokenizer.tokenize(gts)
res = tokenizer.tokenize(res)
# =================================================
# Set up scorers
# =================================================
print('setting up scorers...')
'''
scorers = [
(Bleu(4), ["Bleu_1", "Bleu_2", "Bleu_3", "Bleu_4"]),
(Meteor(),"METEOR"),
(Rouge(), "ROUGE_L"),
(Cider(), "CIDEr")
]
'''
scorers = [(Bleu(4), ["Bleu_1", "Bleu_2", "Bleu_3", "Bleu_4"])]
# =================================================
# Compute scores
# =================================================
for scorer, method in scorers:
print('computing %s score...' % (scorer.method()))
score, scores = scorer.compute_score(gts, res)
if type(method) == list:
for sc, scs, m in zip(score, scores, method):
self.setEval(sc, m)
self.setImgToEvalImgs(scs, gts.keys(), m)
print("%s: %0.3f" % (m, sc))
else:
self.setEval(score, method)
self.setImgToEvalImgs(scores, gts.keys(), method)
print("%s: %0.3f" % (method, score))
self.setEvalImgs()
def test_score():
# init all argument
data = read_json()
rouge_eval = RougeL()
bleu_eval = Bleu()
for idx, (ref_key, cand_key) in enumerate(data):
ref_sent = data[idx][ref_key]
cand_sent = data[idx][cand_key]
rouge_eval.add_inst(cand_sent, ref_sent)
bleu_eval.add_inst(cand_sent, ref_sent)
bleu_score = bleu_eval.get_score()
rouge_score = rouge_eval.get_score()
print('bleu score: {}, rouge score: {}'.format(bleu_score, rouge_score))
def score(ref, hypo):
scorers = [
(Bleu(4),["Bleu_1","Bleu_2","Bleu_3","Bleu_4"]),
(Meteor(),"METEOR"),
(Rouge(),"ROUGE_L"),
(Cider(),"CIDEr")
]
final_scores = {}
for scorer,method in scorers:
score,scores = scorer.compute_score(ref,hypo)
if type(score)==list:
for m,s in zip(method,score):
final_scores[m] = s
else:
final_scores[method] = score
return final_scores
def cal_avg_B4(custom_gts, custom_res):
# input tested senetences, and (top_N - 1) corresponding 'gt' sentences
# return the BLEU-4 score
# calculate BLEU scores in tradictional way
gts = tokenizer.tokenize(custom_gts)
res = tokenizer.tokenize(custom_res)
print('setting up scorers...')
scorers = [(Bleu(4), ["Bleu_1", "Bleu_2", "Bleu_3", "Bleu_4"])]
imgToEval = {}
for scorer, method in scorers:
print('computing %s score...'%(scorer.method()))
if type(method) == list:
score, scores, subgraph_training_bleu = scorer.compute_score(gts, res)
for sc, scs, m in zip(score, scores, method):
setImgToEvalImgs(scs, list(gts.keys()), m, imgToEval)
print("%s: %0.3f"%(m, sc))
B_4s = [imgToEval[sen_id]['Bleu_4'] for sen_id in custom_gts.keys()]
return B_4s
def score(ref, hypo):
"""
ref, dictionary of reference sentences (id, sentence)
hypo, dictionary of hypothesis sentences (id, sentence)
score, dictionary of scores
"""
scorers = [
(Bleu(4), ["Bleu_1", "Bleu_2", "Bleu_3", "Bleu_4"]),
(Rouge(), "ROUGE_L"),
]
final_scores = {}
for scorer, method in scorers:
score, scores = scorer.compute_score(ref, hypo)
if type(score) == list:
for m, s in zip(method, score):
final_scores[m] = s
else:
final_scores[method] = score
return final_scores
def test_count_bp():
cand = '我是中国人'
ref = '重视啊啊啊啊我啊啊我了'
bleu = Bleu(N_SIZE)
bp = bleu.count_bp(cand, ref)
print('BP: {}'.format(bp))
def __init__(self, alpha=0.5):
self.simple_meteor = SimpleMeteor(alpha=alpha, beta=0.16)
self.tri_bleu = Bleu(3)
self.four_bleu = Bleu(4, beta=0.13)
self.p = Preprocessor()
def __init__(self):
self.scorers = [(Bleu(4), ["Bleu_1", "Bleu_2", "Bleu_3", "Bleu_4"])]
def test_score():
cand = "中华人民共和国"
ref = "中华人民共和国公民"
bleu = Bleu(N_SIZE)
s = bleu.score(cand, ref)
print('score: {}'.format(s))
def init_scorer(cached_tokens):
global CiderD_scorer
CiderD_scorer = CiderD_scorer or CiderD(df=cached_tokens)
global Bleu_scorer
Bleu_scorer = Bleu_scorer or Bleu(4)
from rouge import Rouge
import argparse
import logging
from ReadingComprehension.IterativeReattentionAligner.e2e_encoder import MnemicReader as e2e_MnemicReader
import cProfile, pstats, io
from utils import *
from InformationRetrieval.AttentionRM.modules import AttentionRM
from EndToEndModel.modules import EndToEndModel
from nltk.translate.bleu_score import sentence_bleu
import re
import pickle
from CSMrouge import RRRouge
from bleu import Bleu
stoplist = set(['.',',', '...', '..'])
bleu_obj = Bleu(4)
def add_arguments(parser):
parser.add_argument("train_file", help="File that contains training data")
parser.add_argument("dev_file", help="File that contains dev data")
parser.add_argument("embedding_file", help="File that contains pre-trained embeddings")
parser.add_argument('--dicts_dir', type=str, default=None, help='Directory containing the word dictionaries')
parser.add_argument('--seed', type=int, default=6, help='Random seed for the experiment')
parser.add_argument('--epochs', type=int, default=20, help='Train data iterations')
parser.add_argument('--train_batch_size', type=int, default=32, help='Batch size for training')
parser.add_argument('--dev_batch_size', type=int, default=32, help='Batch size for dev')
parser.add_argument('--hidden_size', type=int, default=100, help='Hidden size for LSTM')
parser.add_argument('--num_layers', type=int, default=1, help='Number of layers for LSTM')
parser.add_argument('--char_emb_size', type=int, default=50, help='Embedding size for characters')
parser.add_argument('--pos_emb_size', type=int, default=50, help='Embedding size for pos tags')
parser.add_argument('--ner_emb_size', type=int, default=50, help='Embedding size for ner')
