/
eval_multi.py
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eval_multi.py
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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
import torch.nn as nn
import numpy as np
import json
from json import encoder
import random
import string
import time
import os
import sys
import misc.utils as utils
from eval_utils import getCOCO
from misc.div_utils import compute_div_n, compute_global_div_n
import sys
try:
sys.path.append("coco-caption")
annFile = 'coco-caption/annotations/captions_val2014.json'
from pycocotools.coco import COCO
from pycocoevalcap.eval import COCOEvalCap
from pycocoevalcap.eval_spice import COCOEvalCapSpice
from pycocoevalcap.tokenizer.ptbtokenizer import PTBTokenizer
from pycocoevalcap.bleu.bleu import Bleu
sys.path.append("cider")
from pyciderevalcap.cider.cider import Cider
except:
print('Warning: requirements for eval_multi not satisfied')
def eval_allspice(dataset, preds_n, model_id, split):
coco = getCOCO(dataset)
valids = coco.getImgIds()
capsById = {}
for d in preds_n:
capsById[d['image_id']] = capsById.get(d['image_id'], []) + [d]
# filter results to only those in MSCOCO validation set (will be about a third)
preds_filt_n = [p for p in preds_n if p['image_id'] in valids]
print('using %d/%d predictions_n' % (len(preds_filt_n), len(preds_n)))
cache_path_n = os.path.join('eval_results/', model_id + '_' + split + '_n.json')
json.dump(preds_filt_n, open(cache_path_n, 'w')) # serialize to temporary json file. Sigh, COCO API...
# Eval AllSPICE
cocoRes_n = coco.loadRes(cache_path_n)
cocoEvalAllSPICE = COCOEvalCapSpice(coco, cocoRes_n)
cocoEvalAllSPICE.params['image_id'] = cocoRes_n.getImgIds()
cocoEvalAllSPICE.evaluate()
out = {}
for metric, score in cocoEvalAllSPICE.eval.items():
out['All'+metric] = score
imgToEvalAllSPICE = cocoEvalAllSPICE.imgToEval
# collect SPICE_sub_score
for k in list(imgToEvalAllSPICE.values())[0]['SPICE'].keys():
if k != 'All':
out['AllSPICE_'+k] = np.array([v['SPICE'][k]['f'] for v in imgToEvalAllSPICE.values()])
out['AllSPICE_'+k] = (out['AllSPICE_'+k][out['AllSPICE_'+k]==out['AllSPICE_'+k]]).mean()
for p in preds_filt_n:
image_id, caption = p['image_id'], p['caption']
imgToEvalAllSPICE[image_id]['caption'] = capsById[image_id]
return {'overall': out, 'imgToEvalAllSPICE': imgToEvalAllSPICE}
def eval_oracle(dataset, preds_n, model_id, split):
cache_path = os.path.join('eval_results/', model_id + '_' + split + '_n.json')
coco = getCOCO(dataset)
valids = coco.getImgIds()
capsById = {}
for d in preds_n:
capsById[d['image_id']] = capsById.get(d['image_id'], []) + [d]
sample_n = capsById[list(capsById.keys())[0]]
for i in range(len(capsById[list(capsById.keys())[0]])):
preds = [_[i] for _ in capsById.values()]
json.dump(preds, open(cache_path, 'w')) # serialize to temporary json file. Sigh, COCO API...
cocoRes = coco.loadRes(cache_path)
cocoEval = COCOEvalCap(coco, cocoRes)
cocoEval.params['image_id'] = cocoRes.getImgIds()
cocoEval.evaluate()
imgToEval = cocoEval.imgToEval
for img_id in capsById.keys():
tmp = imgToEval[img_id]
for k in tmp['SPICE'].keys():
if k != 'All':
tmp['SPICE_'+k] = tmp['SPICE'][k]['f']
if tmp['SPICE_'+k] != tmp['SPICE_'+k]: # nan
tmp['SPICE_'+k] = -100
tmp['SPICE'] = tmp['SPICE']['All']['f']
if tmp['SPICE'] != tmp['SPICE']: tmp['SPICE'] = -100
capsById[img_id][i]['scores'] = imgToEval[img_id]
out = {'overall': {}, 'ImgToEval': {}}
for img_id in capsById.keys():
out['ImgToEval'][img_id] = {}
for metric in capsById[img_id][0]['scores'].keys():
if metric == 'image_id': continue
out['ImgToEval'][img_id]['oracle_'+metric] = max([_['scores'][metric] for _ in capsById[img_id]])
out['ImgToEval'][img_id]['avg_'+metric] = sum([_['scores'][metric] for _ in capsById[img_id]]) / len(capsById[img_id])
out['ImgToEval'][img_id]['captions'] = capsById[img_id]
for metric in list(out['ImgToEval'].values())[0].keys():
if metric == 'captions':
continue
tmp = np.array([_[metric] for _ in out['ImgToEval'].values()])
tmp = tmp[tmp!=-100]
out['overall'][metric] = tmp.mean()
return out
def eval_div_stats(dataset, preds_n, model_id, split):
tokenizer = PTBTokenizer()
capsById = {}
for i, d in enumerate(preds_n):
d['id'] = i
capsById[d['image_id']] = capsById.get(d['image_id'], []) + [d]
n_caps_perimg = len(capsById[list(capsById.keys())[0]])
print(n_caps_perimg)
_capsById = capsById # save the untokenized version
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))
# compute mbleu
scorer = Bleu(4)
all_scrs = []
scrperimg = np.zeros((n_caps_perimg, len(capsById)))
for i in range(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)
out = {}
out['overall'] = {'Div1': div_1, 'Div2': div_2, 'gDiv1': globdiv_1}
for k, score in zip(range(4), all_scrs.mean(axis=0).tolist()):
out['overall'].update({'mBLeu_%d'%(k+1): score})
imgToEval = {}
for i,imgid in enumerate(capsById.keys()):
imgToEval[imgid] = {'mBleu_2' : scrperimg[:,i].mean()}
imgToEval[imgid]['individuals'] = []
for j, d in enumerate(_capsById[imgid]):
imgToEval[imgid]['individuals'].append(preds_n[d['id']])
imgToEval[imgid]['individuals'][-1]['mBleu_2'] = scrperimg[j,i]
out['ImgToEval'] = imgToEval
print('Mean mutual Bleu scores on this set is:\nmBLeu_1, mBLeu_2, mBLeu_3, mBLeu_4')
print(all_scrs.mean(axis=0))
return out
def eval_self_cider(dataset, preds_n, model_id, split):
cache_path = os.path.join('eval_results/', model_id + '_' + split + '_n.json')
coco = getCOCO(dataset)
valids = coco.getImgIds()
# Get Cider_scorer
Cider_scorer = Cider(df='corpus')
tokenizer = PTBTokenizer()
gts = {}
for imgId in valids:
gts[imgId] = coco.imgToAnns[imgId]
gts = tokenizer.tokenize(gts)
for imgId in valids:
Cider_scorer.cider_scorer += (None, gts[imgId])
Cider_scorer.cider_scorer.compute_doc_freq()
Cider_scorer.cider_scorer.ref_len = np.log(float(len(Cider_scorer.cider_scorer.crefs)))
# Prepare captions
capsById = {}
for d in preds_n:
capsById[d['image_id']] = capsById.get(d['image_id'], []) + [d]
capsById = tokenizer.tokenize(capsById)
imgIds = list(capsById.keys())
scores = Cider_scorer.my_self_cider([capsById[_] for _ in imgIds])
def get_div(eigvals):
eigvals = np.clip(eigvals, 0, None)
return -np.log(np.sqrt(eigvals[-1]) / (np.sqrt(eigvals).sum())) / np.log(len(eigvals))
sc_scores = [get_div(np.linalg.eigvalsh(_/10)) for _ in scores]
score = np.mean(np.array(sc_scores))
imgToEval = {}
for i, image_id in enumerate(imgIds):
imgToEval[image_id] = {'self_cider': sc_scores[i], 'self_cider_mat': scores[i].tolist()}
return {'overall': {'self_cider': score}, 'imgToEval': imgToEval}
return score