def __init__(
self,
image_ids_path, #
input_seq, #
target_seq, #
gv_feat_path, #''
att_feats_folder, #
seq_per_img,
max_feat_num):
self.max_feat_num = max_feat_num
self.seq_per_img = seq_per_img
self.image_ids = utils.load_lines(image_ids_path) # a list of str
self.att_feats_folder = att_feats_folder if len(
att_feats_folder) > 0 else None
self.gv_feat = pickle.load(
open(gv_feat_path,
'rb'), encoding='bytes') if len(gv_feat_path) > 0 else None
if input_seq is not None and target_seq is not None:
self.input_seq = pickle.load(open(input_seq, 'rb'),
encoding='bytes')
self.target_seq = pickle.load(open(target_seq, 'rb'),
encoding='bytes')
self.seq_len = 0 #len(self.input_seq[self.image_ids[0]][0,:])
else:
self.seq_len = -1
self.input_seq = None
self.target_seq = None
return
def __init__(
self,
eval_ids,
gv_feat,
att_feats,
eval_annfile,
dataset_name
):
super(Evaler, self).__init__()
self.vocab = utils.load_vocab(cfg.INFERENCE.VOCAB)
self.eval_ids = np.array(utils.load_lines(eval_ids))#np.array(utils.load_ids(eval_ids))
self.eval_loader = data_loader.load_val(eval_ids, gv_feat, att_feats, dataset_name)
self.evaler = evaluation.create(cfg.INFERENCE.EVAL, eval_annfile)
self.dataset_name = dataset_name
def __init__(
self,
eval_ids,
gv_feat,
att_feats,
dataset_name,
eval_annfile=None
):
super(Evaler, self).__init__()
self.vocab = utils.load_vocab(cfg.INFERENCE.VOCAB)
if dataset_name == 'raw':
self.eval_ids = np.array([img.split('.')[0] for img in os.listdir(eval_ids)])
self.evaler = None
else:
self.eval_ids = np.array(utils.load_lines(eval_ids))#np.array(utils.load_ids(eval_ids))
self.evaler = evaluation.create(dataset_name, eval_annfile)
self.eval_loader = data_loader.load_val(eval_ids, gv_feat, att_feats, dataset_name)
self.dataset_name = dataset_name
def __init__(
self,
image_ids_path, #/data/disk1/private/FXData/COCO/karpathy_image_ids/coco_train_image_id.txt
input_seq, #/data/disk1/private/FXData/COCO/sent/coco_train_input.pkl
target_seq, #/data/disk1/private/FXData/COCO/sent/coco_train_target.pkl
gv_feat_path, #''
att_feats_folder, #/data/disk1/private/FXData/COCO
seq_per_img,
max_feat_num,
id2name_path, #/data/disk1/private/FXData/COCO/id2name_123287.json
annotation_path):
super().__init__(image_ids_path, input_seq, target_seq, gv_feat_path,
att_feats_folder, seq_per_img, max_feat_num)
self.annotation_path = annotation_path
with open(
os.path.join(self.annotation_path, 'bboxes_coco_123287.json'),
'r') as f:
self.annotation_bboxes = json.load(f)
self.id2category = utils.load_lines(
os.path.join(self.annotation_path, 'id2category.txt'))
with open(id2name_path, 'r') as f:
self.id2name = json.load(f)