VTimeLLM/dvc_eval/SODA/soda.py

#!/uer/bin/env python
import argparse
import json
from tqdm import tqdm
import numpy as np

from pycocoevalcap.tokenizer.ptbtokenizer import PTBTokenizer
from pycocoevalcap.meteor.meteor import Meteor
from pycocoevalcap.cider.cider import Cider

from .dataset import ANETCaptions
from .utils import iou, remove_nonascii


class SODA:
    def __init__(self, data, soda_type="c", tious=None, scorer="Meteor", verbose=False, print_matrix=False):
        #self.data = data
        self.preds = data.preds
        self.gts = data.gts
        self.gt_vids = data.gt_vids
        self.soda_type = soda_type
        self.tious = [0.0] if tious is None else tious
        self.tokenizer = PTBTokenizer()
        if scorer == "BertScore":
            from nlpeval.bert_r_score import BertScore
        self.scorer = eval(scorer)()
        self.scorer_name = scorer
        self.verbose = verbose
        self.print_matrix = print_matrix

        if soda_type == "a":    # averaging F-measure scores with IoU threshold = 0.9, 0.7, 0.5, 0.3
            self.soda_func = self.soda_a
        elif soda_type == "b":  # F-measure, where IoU threshold is set to 0.
            self.soda_func = self.soda_b
        elif soda_type == "c":  # F-measure, utilizing the IoU x METEOR score
            self.soda_func = self.soda_c
        elif soda_type == "d":  # F-measure of IoU score
            self.soda_func = self.soda_d

            class Dummy:
                def compute_score(self, x, y):
                    return [0, 0]

            self.scorer = Dummy()
        else:
            raise NotImplementedError

    @classmethod
    def build(cls, preds, gts, gt_vids, soda_type="c", tious=[0.0], scorer="Meteor", verbose=False):
        data = ANETCaptions(preds, gts, gt_vids)
        data.preprocess()
        return cls(data, soda_type, tious, scorer, verbose)

    @classmethod
    def build_from_prediction(cls, preds, gt_files, soda_type="c", tious=[0.0], scorer="Meteor", verbose=False):
        data = ANETCaptions.from_prediction(gt_files, preds)
        data.preprocess()
        return cls(data, soda_type, tious, scorer, verbose)

    def calc_iou_matrix(self, preds, golds):
        #print(preds["timestamps"], gt["timestamps"])
        return np.array([[iou(pred, ct) for pred in preds["timestamps"]] for ct in golds['timestamps']])

    def calc_score_matrix(self, preds, golds):
        # Reformat to fit the input of pycocoevalcap scorers.
        p_sent, g_sent = preds["sentences"], golds["sentences"]
        res = {index: p for index, p in enumerate(p_sent)}
        gts = [{index: g for index in range(len(p_sent))} for i, g in enumerate(g_sent)]
        return np.array([self.scorer.compute_score(res, gt)[1] for gt in gts])

    def evaluate(self,):

        final = []

        if self.verbose:
            print(f"\n| Running SODA {self.soda_type}.")
        tious = self.tious
        p_best = [[] for i in range(len(tious))]
        r_best = [[] for i in range(len(tious))]
        f_best = [[] for i in range(len(tious))]
        n_pred = []
        for vid in tqdm(self.gt_vids, disable=not self.verbose):
            _p = [[] for i in range(len(tious))]
            _r = [[] for i in range(len(tious))]
            _f = [[] for i in range(len(tious))]
            pred = self.preds[vid]
            n_pred.append(len(pred["sentences"]))
            # empty pred
            if not pred['sentences']:
                for i, tiou in enumerate(tious):
                    p_best[i].append(0)
                    r_best[i].append(0)
                    f_best[i].append(0)
                continue
            for gt in self.gts:
                if vid not in gt:
                    continue
                gold = gt[vid]
                # create matrix
                _iou = self.calc_iou_matrix(pred, gold)
                scores = self.calc_score_matrix(pred, gold)
                
                for i, tiou in enumerate(tious):
                    iou = np.copy(_iou)
                    iou[iou < tiou] = 0.0
                    try:
                        max_score, pairs = self.soda_func(iou, scores)
                    except:  # RecursionError
                        max_score, pairs = 0., None
                    (n_g, n_p) = iou.shape
                    p = max_score / n_p
                    r = max_score / n_g
                    _p[i].append(p)
                    _r[i].append(r)
                    _f[i].append(2 * p * r / (p + r) if p+r > 0 else 0)
            best_idx = np.argmax(_f, axis=1)
            for i, tiou in enumerate(tious):
                p_best[i].append(_p[i][best_idx[i]])
                r_best[i].append(_r[i][best_idx[i]])
                f_best[i].append(_f[i][best_idx[i]])


                if self.print_matrix:

                    if vid in ['v_5pzCqex6S48', 'v_xCplsH6deic']:
                        print("=" * 100)
                        print(f'Video ID: {vid}')
                        print("======= IOU Matrix =======")
                        print(_iou * 100)
                        print("\n======= SCORE Matrix =======")
                        print(scores * 100)
                        print("\n======= COST Matrix =======")
                        print(_iou * scores * 100)
                        print()
                        print(f'Precision: {p * 100}\nRecall: {r * 100}\nF1-score: {_f[i][0] * 100}')
                        print("Prediction")

                        for (p_s, p_t) in zip(pred['sentences'], pred['timestamps']):
                            print(f'Time: {p_t[0]:.1f}, {p_t[1]:.1f} {p_s}')

                        print()
                        print("Ground-truth")
                        for (p_s, p_t) in zip(gold['sentences'], gold['timestamps']):
                            print(f'Time: {p_t[0]:.1f}, {p_t[1]:.1f} {p_s}')

                    final.append({
                        'vid':vid,
                        'precision': p * 100,
                        'recall': r * 100,
                        'f1-score': _f[i][0] * 100,
                        'pred_sentences': pred['sentences'],
                        'gold_sentences': gold['sentences'],
                        'pred_timestamps': pred['timestamps'],
                        'gold_timestamps': gold['timestamps'],
                    })


        precision = np.mean(p_best, axis=1)
        recall = np.mean(r_best, axis=1)
        f1 = np.mean(f_best, axis=1)
        print(f"avg. outputs: {np.mean(n_pred)}")
        # average scores across all the tIoUs
        if self.verbose:
            for i, tiou in enumerate(tious):
                partial_result = {self.scorer_name: [precision[i], recall[i], f1[i]]}
                print_score(partial_result, description=f"tIoU: {tiou}")

        final_scores = [np.mean(precision), np.mean(recall), np.mean(f1)]
        result = {self.scorer_name: final_scores}
        return result

    def soda_a(self, iou, scores):
        _, pairs = self.chased_dp_assignment(iou)
        r, c = (*zip(*pairs),)
        max_score = np.sum(scores[r, c])
        return max_score, pairs

    def soda_b(self, iou, scores):
        # same as soda_a
        _, pairs = self.chased_dp_assignment(iou)
        r, c = (*zip(*pairs),)
        max_score = np.sum(scores[r, c])
        return max_score, pairs

    def soda_c(self, iou, scores):
        max_score, pairs = self.chased_dp_assignment(iou*scores)
        return max_score, pairs

    def soda_d(self, iou, scores):
        max_score, pairs = self.chased_dp_assignment(iou)
        return max_score, pairs

    def chased_dp_assignment(self, scores):
        """ 
        Run dp matching
        Recurrence:  
            dp[i,j] = 
                max(dp[i-1,j], dp[i-1,j-1] + scores[i,j], dp[i,j-1])
        """
        M, N = scores.shape
        dp = - np.ones((M, N))
        path = np.zeros((M, N))

        def transition(i, j):
            if dp[i, j] >= 0:
                return dp[i, j]
            elif i == 0 and j == 0:
                state = [-1, -1, scores[i, j]]
            elif i == 0:
                state = [-1, transition(i, j-1), scores[i, j]]
            elif j == 0:
                state = [transition(i-1, j), -1, scores[i, j]]
            else:
                state = [transition(i-1, j), transition(i, j-1), transition(i-1, j-1) + scores[i, j]]
            dp[i, j] = np.max(state)
            path[i, j] = np.argmax(state)
            return dp[i, j]

        def get_pairs(i, j):
            p = np.where(path[i][:j+1] == 2)[0]
            if i != 0 and len(p) == 0:
                return get_pairs(i-1, j)
            elif i == 0 or p[-1] == 0:
                return [(i, p[-1])]
            else:
                return get_pairs(i-1, p[-1]-1) + [(i, p[-1])]
        N, M = scores.shape
        max_score = transition(N-1, M-1)
        pairs = get_pairs(N-1, M-1)
        return max_score, pairs


def print_score(result, description="SODA result"):
    prf = ["precision", "recall", "f1_score"]
    print('-' * 80)
    print(description)
    print('-' * 80)
    for scorer_name, score in result.items():
        print(f'| scorer:{scorer_name}')
        for k, v in zip(prf, score):
            print(f"\t{k}:{v*100:2.4f}")


def main(args):
    # Call coco eval
    data = ANETCaptions.from_load_files(args.references,
                                        args.prediction,
                                        multi_reference=args.multi_reference,
                                        verbose=args.verbose,
                                        )
    data.preprocess()
    if args.soda_type == 'a':
        tious = args.tious
    else:
        tious = None
    evaluator = SODA(data,
                     soda_type=args.soda_type,
                     tious=tious,
                     scorer=args.metric,
                     verbose=args.verbose
                     )
    result = evaluator.evaluate()
    print_score(result)


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('-p', '--prediction', type=str, required=True, default='sample.json',
                        help='system output file with json format for ActivityNet Challenge')
    parser.add_argument('-r', '--references', type=str, nargs='+', default=['./data/val_1.json', './data/val_2.json'],
                        help='reference files with ground truth captions')
    parser.add_argument('-m', '--metric', type=str, default="Meteor", choices=['Meteor', 'Cider',  'BertScore'],
                        help='choice evaluation metrics for SODA')
    parser.add_argument('-s', '--soda_type', type=str, default="c", choices=['a', 'b',  'c', 'd'],
                        help='choice evaluation metrics for SODA')
    parser.add_argument('--tious', type=float,  nargs='+', default=[0.3, 0.5, 0.7, 0.9],
                        help='list of the tIoUs (only for SODA-a)')
    parser.add_argument('-mr', '--multi_reference', action='store_true',
                        help='print details')
    parser.add_argument('-v', '--verbose', action='store_true',
                        help='print details')
    args = parser.parse_args()

    main(args)