def _do_mscnn_eval(self, file_dict):
"""
file_dict: 'cls' -> 'mscnn eval file'
"""
anno_filename = self._label_file
image_set_file = self._get_image_set_file()
aps = []
use_07_metric = self.config['use_07_metric']
print 'VOC07 metric? ' + ('Yes' if use_07_metric else 'No')
print('~~~~~~~~')
weighted_ap = 0
weighted_n = 0
for i, cls in enumerate(self._classes):
if cls == '__background__':
continue
if cls in file_dict:
filename = file_dict[cls]
else:
print('No file found for {}'.format(cls))
continue
rec, prec, ap, ground_truth_box_num = sz_eval(
filename,
anno_filename,
image_set_file,
cls,
ovthresh=0.5,
use_07_metric=use_07_metric)
aps += [ap]
weighted_n += ground_truth_box_num
weighted_ap += ground_truth_box_num * ap
print('AP for %s = %f (%d samples)' %
(cls, ap, ground_truth_box_num))
print('Mean AP = {:.4f}'.format(np.mean(aps)))
print('Weighted AP = {:.4f}'.format(float(weighted_ap) / weighted_n))
print('~~~~~~~~')
def _do_python_eval(self, output_dir='output'):
anno_filename = self._label_file
image_set_file = self._get_image_set_file()
aps = []
use_07_metric = self.config['use_07_metric']
print 'VOC07 metric? ' + ('Yes' if use_07_metric else 'No')
if not os.path.isdir(output_dir):
os.mkdir(output_dir)
print('~~~~~~~~')
weighted_ap = 0
weighted_n = 0
for i, cls in enumerate(self._classes):
if cls == '__background__':
continue
filename = self._get_sz_results_file_template().format(cls)
rec, prec, ap, ground_truth_box_num = sz_eval(
filename,
anno_filename,
image_set_file,
cls,
ovthresh=0.5,
use_07_metric=use_07_metric)
aps += [ap]
weighted_n += ground_truth_box_num
weighted_ap += ground_truth_box_num * ap
print('AP for %s = %f (%d samples)' %
(cls, ap, ground_truth_box_num))
with open(os.path.join(output_dir, cls + '_pr.pkl'), 'w') as f:
cPickle.dump({'rec': rec, 'prec': prec, 'ap': ap}, f)
print('Mean AP = {:.4f}'.format(np.mean(aps)))
print('Weighted AP = {:.4f}'.format(float(weighted_ap) / weighted_n))
print('~~~~~~~~')