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#!/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)
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