def eval_scores(score_files, score_weights, agg_method):
"""Fuse the score files of different models
Args:
list(str) score_files: file names of score files
list(float) score_weights: weights of score files
str agg_method: the name of method for aggregating the segment level scores
This is because the current used scores are segment level scores. See test_models.py.
Returns:
int: the fused accuracy.
"""
score_npz_files = [np.load(x) for x in score_files]
if score_weights is None:
score_weights = [1] * len(score_npz_files)
else:
if len(score_weights) != len(score_npz_files):
raise ValueError(
"Only {} weight specifed for a total of {} score files".format(
len(score_weights), len(score_npz_files)))
score_list = [x['scores'][:, 0] for x in score_npz_files
] # x['scores'] has two columns [segment level score, label]
label_list = [x['labels'] for x in score_npz_files]
# label verification
# score_aggregation
agg_score_list = []
for score_vec in score_list:
agg_score_vec = [
default_aggregation_func(x,
normalization=False,
crop_agg=getattr(np, agg_method))
for x in score_vec
] #lz:video level scores
agg_score_list.append(np.array(agg_score_vec))
final_scores = np.zeros_like(agg_score_list[0])
for i, agg_score in enumerate(agg_score_list):
final_scores += agg_score * score_weights[i]
# accuracy
acc = mean_class_accuracy(final_scores, label_list[0])
# softmax score
softmax_scores = [softmax(vec) for vec in final_scores]
return acc, softmax_scores
def get_score_11111(score_files, xxxx=0.4):
crop_agg = "mean"
score_npz_files = [np.load(x) for x in score_files]
score_list = [x['scores'][:, 0] for x in score_npz_files]
label_list = [x['labels'] for x in score_npz_files]
agg_score_list = []
for score_vec in score_list:
agg_score_vec = [
default_aggregation_func(x,
normalization=False,
crop_agg=getattr(np, crop_agg))
for x in score_vec
]
agg_score_list.append(np.array(agg_score_vec))
split = score_files[0].split("_")[2]
score_weights = [xxxx, 1.0 - xxxx]
if score_weights is None:
score_weights = [1] * len(score_npz_files)
else:
score_weights = score_weights
if len(score_weights) != len(score_npz_files):
raise ValueError(
"Only {} weight specifed for a total of {} score files".format(
len(score_weights), len(score_npz_files)))
final_scores = np.zeros_like(agg_score_list[0])
for i, agg_score in enumerate(agg_score_list):
final_scores += agg_score * score_weights[i]
print "split: ", split
ff = [x[0][0] for x in final_scores]
acc = mean_class_accuracy(ff, label_list[0])
# print 'Final accuracy {:02f}%'.format(acc * 100)
# print "rgb_score_weight: ", xxxx
# print "\n"
return acc
video_pred = [np.argmax(x) for x in final_scores]
video_labels = label_list[0]
cf = confusion_matrix(video_labels, video_pred).astype(float)
print cf
# accuracy for each class
cls_cnt = cf.sum(axis=1)
cls_hit = np.diag(cf)
cls_acc = cls_hit / cls_cnt
cf_acc = cf / cls_cnt
print cls_acc
# accuracy
acc = mean_class_accuracy(final_scores, label_list[0])
print 'Final accuracy {:02f}%'.format(acc * 100)
# save file
if len(score_npz_files) == 2:
file_path = os.path.dirname(args.score_files[0])
file_name = 'com' + os.path.splitext(
args.score_files[0])[0].split('_split')[1] + '.txt'
with open(file_path + '/' + file_name, 'w') as f:
f.write('Confusion Matrix\n')
f.write('%s\n' % cf)
f.write('Confusion Matrix with accuracy\n')
f.write('%s\n' % cf_acc)
f.write('\nClass accuracy = \n%s\n' % cls_acc)
f.write('\nFinal accuracy {:02f}%'.format(acc * 100))
parser.add_argument('--score_weights', nargs='+', type=float, default=None)
args = parser.parse_args()
score_npz_files = [np.load(x) for x in args.score_files]
if args.score_weights is None:
score_weights = [1] * len(score_npz_files)
else:
score_weights = args.score_weights
if len(score_weights) != len(score_npz_files):
raise ValueError("Only {} weight specifed for a total of {} score files"
.format(len(score_weights), len(score_npz_files)))
score_list = [x['scores'][:, 0] for x in score_npz_files]
label_list = [x['labels'] for x in score_npz_files]
# label verification
# score_aggregation
agg_score_list = []
for score_vec in score_list:
agg_score_vec = [default_aggregation_func(x, normalization=False) for x in score_vec]
agg_score_list.append(np.array(agg_score_vec))
final_scores = np.zeros_like(agg_score_list[0])
for i, agg_score in enumerate(agg_score_list):
final_scores += agg_score * score_weights[i]
# accuracy
acc = mean_class_accuracy(final_scores, label_list[0])
print 'Final accuracy {:02f}%'.format(acc * 100)
if len(score_weights) != len(score_npz_files):
raise ValueError(
"Only {} weight specifed for a total of {} score files".format(
len(score_weights), len(score_npz_files)))
final_scores = np.zeros_like(agg_score_list[0])
for i, agg_score in enumerate(agg_score_list):
final_scores += agg_score * score_weights[i]
print "split: ", split
# accuracy
# for x in final_scores:
# xx = x[0]
# xxx = xx[0]
ff = [x[0][0] for x in final_scores]
acc, class_acc = mean_class_accuracy(ff, label_list[0])
print 'Final accuracy {:02f}%'.format(acc * 100)
print "rgb_score_weight: ", xxxx
print "\n"
# MIFS fusion with our method ####
# xxxx = 0.4
# score_weights = [xxxx, 1.0-xxxx]
# if score_weights is None:
# score_weights = [1] * len(score_npz_files)
# else:
# score_weights = score_weights
# if len(score_weights) != len(score_npz_files):
# raise ValueError("Only {} weight specifed for a total of {} score files"
# .format(len(score_weights), len(score_npz_files)))
#
