eval_multi.py

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_spice_n(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 Spice_n
    cocoRes_n = coco.loadRes(cache_path_n)
    cocoEvalSpice_n = COCOEvalCapSpice(coco, cocoRes_n)
    cocoEvalSpice_n.params['image_id'] = cocoRes_n.getImgIds()
    cocoEvalSpice_n.evaluate()

    out = {}
    for metric, score in cocoEvalSpice_n.eval.items():
        out[metric+'_n'] = score

    imgToEvalSpice_n = cocoEvalSpice_n.imgToEval
    # collect SPICE_sub_score
    for k in imgToEvalSpice_n.values()[0]['SPICE'].keys():
        if k != 'All':
            out['SPICE_n_'+k] = np.array([v['SPICE'][k]['f'] for v in imgToEvalSpice_n.values()])
            out['SPICE_n_'+k] = (out['SPICE_n_'+k][out['SPICE_n_'+k]==out['SPICE_n_'+k]]).mean()
    for p in preds_filt_n:
        image_id, caption = p['image_id'], p['caption']
        imgToEvalSpice_n[image_id]['caption'] = capsById[image_id]
    return {'overall': out, 'imgToEvalSpice_n': imgToEvalSpice_n}

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[capsById.keys()[0]]
    for i in range(len(capsById[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 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[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