class BleuCoco(Metrics):
def __init__(self, test_text='', annotation_file='', gram=3):
super().__init__()
self.name = 'BleuCoco'
self.coco = COCO(annotation_file)
self.test_data = json.load(open(test_text, 'r'))
self.gram = gram
def get_score(self, is_fast=True, ignore=False):
if ignore:
return 0
return self.get_bleu_parallel()
def calc_bleu(self, reference, hypothesis, weight):
return nltk.translate.bleu_score.sentence_bleu(reference, hypothesis, weight,
smoothing_function=SmoothingFunction().method1)
def get_bleu(self):
ngram = self.gram
bleu = list()
weight = tuple((1. / ngram for _ in range(ngram)))
for hypothesis in self.test_data:
annIds = self.coco.getAnnIds(imgIds=hypothesis['id'])
anns = self.coco.loadAnns(annIds)
bleu.append(self.calc_bleu(anns, hypothesis['caption'], weight))
return sum(bleu) / len(bleu)
def get_bleu_parallel(self, reference=None):
ngram = self.gram
weight = tuple((1. / ngram for _ in range(ngram)))
pool = Pool(os.cpu_count())
result = list()
with open(self.test_data) as test_data:
for hypothesis in test_data:
annIds = self.coco.getAnnIds(imgIds=hypothesis['id'])
anns = self.coco.loadAnns(annIds)
result.append(pool.apply_async(self.calc_bleu, args=(anns, hypothesis['caption'], weight)))
score = 0.0
cnt = 0
for i in result:
score += i.get()
cnt += 1
pool.close()
pool.join()
return score / cnt
def coco_to_roidb(annotation_path,
save_roidb_path,
task='det',
data_dir='',
need_mask=True):
assert task in ['det', 'kps']
coco = COCO(annotation_path)
image_ids = coco.getImgIds()
cats = [cat['name'] for cat in coco.loadCats(coco.getCatIds())]
classes = ['__background__'] + cats
num_classes = len(classes)
class_to_ind = dict(zip(classes, xrange(num_classes)))
class_to_coco_ind = dict(zip(cats, coco.getCatIds()))
coco_ind_to_class_ind = dict([(class_to_coco_ind[cls], class_to_ind[cls])
for cls in classes[1:]])
roidb = []
for i, image_id in enumerate(image_ids):
if i % 1000 == 0:
print('{}/{}'.format(i, len(image_ids)))
im_ann = coco.loadImgs(image_id)[0]
width = im_ann['width']
height = im_ann['height']
annIds = coco.getAnnIds(imgIds=image_id, iscrowd=None)
objs = coco.loadAnns(annIds)
# sanitize bboxes
valid_objs = []
areas_ = []
for obj in objs:
if task == 'kps':
assert obj['category_id'] == 1
assert obj['area'] > 0
x, y, w, h = obj['bbox']
x1 = np.max((0, x))
y1 = np.max((0, y))
x2 = np.min((width - 1, x1 + np.max((0, w - 1))))
y2 = np.min((height - 1, y1 + np.max((0, h - 1))))
if obj['area'] > 0 and x2 >= x1 and y2 >= y1:
obj['clean_bbox'] = [x1, y1, x2, y2]
valid_objs.append(obj)
areas_.append(obj['area'])
# x, y, w, h = obj['bbox']
# x1 = x
# y1 = y
# x2 = x1 + w - 1
# y2 = y1 + h - 1
# assert 0 <= x1 < width
# assert 0 <= y1 < height
# assert 0 <= x2 < width
# assert 0 <= y2 < height
# assert x2 >= x1 and y2 >= y1
# obj['clean_bbox'] = [x1, y1, x2, y2]
# valid_objs.append(obj)
# areas_.append(obj['area'])
objs = valid_objs
num_objs = len(objs)
boxes = np.zeros((num_objs, 4), dtype=np.float32)
gt_classes = np.zeros((num_objs, ), dtype=np.int32)
keypoints = np.zeros((num_objs, 51), dtype=np.float32)
areas = np.array(areas_, dtype=np.float32)
iscrowd = []
for ix, obj in enumerate(objs):
cls = -coco_ind_to_class_ind[obj['category_id']]\
if obj['iscrowd'] else coco_ind_to_class_ind[obj['category_id']]
iscrowd.append(obj['iscrowd'])
boxes[ix, :] = obj['clean_bbox']
gt_classes[ix] = cls
if task == 'kps':
keypoints[ix, :] = obj['keypoints']
roi_rec = {
'image': data_dir + im_ann['file_name'],
'id': im_ann['id'],
'height': height,
'width': width,
'boxes': boxes,
'area': areas,
'iscrowd': np.array(iscrowd, dtype=np.float32),
'gt_classes': gt_classes
}
if task == 'kps':
roi_rec['keypoints'] = keypoints
if need_mask:
roi_rec['gt_masks'] = [x['segmentation'] for x in objs]
roidb.append(roi_rec)
with open(save_roidb_path, 'wb') as fid:
cPickle.dump(roidb, fid, cPickle.HIGHEST_PROTOCOL)